Methods and apparatus to determine an adjustment factor for media impressions

ABSTRACT

Examples to determine media impressions are disclosed. An example method includes detecting a cookie identifier established by a database proprietor at a computing device, determining an impression of media, wherein the impression occurs after the cookie identifier is established, determining a first panelist identifier associated with the impression based on the cookie identifier, determining a second panelist identifier associated with the impression based on determination of a user identity by a panelist meter associated with the computing device, and storing an adjustment factor determined by comparing the first panelist identifier and the second panelist identifier.

RELATED APPLICATION

This patent is a continuation of PCT International Application SerialNo. PCT/US12/26760, entitled “METHODS AND APPARATUS TO DETERMINE MEDIAIMPRESSIONS,” filed Feb. 27, 2012, which claims priority to U.S.Provisional Patent Application Ser. No. 61/454,326, filed on Mar. 18,2011, which are both incorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to determine media impressions.

BACKGROUND

Traditionally, audience measurement entities determine audienceengagement levels for media programming based on registered panelmembers. That is, an audience measurement entity enrolls people whoconsent to being monitored into a panel. The audience measurement entitythen monitors those panel members to determine media (e.g., televisionprograms or radio programs, movies, DVDs, advertisements, etc.) exposedto those panel members. In this manner, the audience measurement entitycan determine exposure measures for different media based on thecollected media measurement data.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other content has evolved significantlyover the years. Some known systems perform such monitoring primarilythrough server logs. In particular, entities serving media on theInternet can use known techniques to log the number of requests receivedfor their media at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system to determine advertisement and/or mediaimpressions using distributed demographic information.

FIG. 2 depicts an example manner of reporting cookies to an audiencemeasurement entity and database proprietor(s) in response to userslogging in to website(s) of the database proprietor(s).

FIG. 3 depicts an example manner in which a web browser can reportimpressions to an impression monitor of the example system of FIG. 1.

FIG. 4 is an example apparatus that may be used to associate impressionswith demographics of users registered with one or more databaseproprietors.

FIG. 5 is an example partner cookie map that may be used by an Internetservice database proprietor to map user identifiers associated with anaudience measurement entity with user identifiers of users registeredwith the Internet service database proprietor.

FIG. 6 is an example impressions table generated by the impressionmonitor system of the example system of FIG. 1 to correlate impressionswith user identifiers of monitored audience members.

FIG. 7 depicts an example partner-based impressions table generated byan Internet service database proprietor to correlate impressions withuser identifiers of registered users of the Internet service databaseproprietors.

FIG. 8 depicts an example impressions table showing quantities ofimpressions associated with monitored users.

FIG. 9 depicts an example campaign-level age/gender and impressioncomposition table generated by a database proprietor.

FIG. 10 is a flow diagram representative of example machine readableinstructions that may be executed to report login events and usercookies to database proprietors.

FIG. 11 is a flow diagram representative of example machine readableinstructions that may be executed to map audience measurement entity(AME) cookie identifiers to user identifiers of users registered with adatabase proprietor.

FIG. 12 is a flow diagram representative of example machine readableinstructions that may be executed to log impressions.

FIG. 13 is a flow diagram representative of example machine readableinstructions that may be executed to generate demographics-basedimpressions reports.

FIG. 14 is an example apparatus that may be used to implement theimpression monitor of FIGS. 1-3.

FIG. 15 is an example apparatus that may be used to implement a cookiereporter of FIG. 2.

FIG. 16 is a block diagram of an example system to generate anadjustment factor.

FIGS. 17 to 25 illustrate example tables that may be generated by thesystem of FIG. 16.

FIGS. 26 to 28 are flow diagrams representative of example machinereadable instructions that may be executed generate adjustmentfactor(s).

FIG. 29 is an example processor system that can be used to execute theexample instructions of FIGS. 10-13 and/or 26-28 to implement theexample apparatus and systems of FIGS. 1, 2, 3, 4, and/or 16.

DETAILED DESCRIPTION

Techniques for monitoring user access to Internet resources such as webpages, advertisements, content and/or other media has evolvedsignificantly over the years. At one point in the past, such monitoringwas done primarily through server logs. In particular, entities servingmedia on the Internet would log the number of requests received fortheir media at their server. Basing Internet usage research on serverlogs is problematic for several reasons. For example, server logs can betampered with either directly or via zombie programs that repeatedlyrequest media from the server to increase the server log counts.Secondly, media is sometimes retrieved once, cached locally and thenrepeatedly viewed from the local cache without involving the server inthe repeat viewings. Server logs cannot track these views of cachedmedia. Thus, server logs are susceptible to both over-counting andunder-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637,fundamentally changed the way Internet monitoring is performed andovercame the limitations of the server side log monitoring techniquesdescribed above. For example, Blumenau disclosed a technique whereinInternet media (e.g., content, advertisements, etc.) to be tracked istagged with beacon instructions (e.g., tag instructions). In particular,monitoring instructions are associated with the HTML of the media (e.g.,advertisements or other Internet content) to be tracked. When a clientrequests the media, both the content and the beacon or tag instructionsare downloaded to the client either simultaneously (e.g., with the taginstructions present in the HTML) or via subsequent requests (e.g., viaexecution of a request to retrieve the monitoring instructions embeddedin the HTML of the content). The tag instructions are, thus, executedwhenever the media is accessed, be it from a server or from a cache.

The tag instructions cause monitoring data reflecting information aboutthe access to the media to be sent from the client that downloaded themedia to a monitoring entity. The monitoring entity may be an audiencemeasurement entity that did not provide the media to the client and whois a trusted third party for providing accurate usage statistics (e.g.,The Nielsen Company, LLC). Advantageously, because the tag instructionsare associated with the media (e.g., embedded in or otherwise linked tosome portion of the media) and executed by the client browser wheneverthe media is accessed, the monitoring information is provided to theaudience measurement company irrespective of whether the client is apanelist of the audience measurement company.

In some instances, it is important to link demographics to themonitoring information. To address this issue, the audience measurementcompany establishes a panel of users who have agreed to provide theirdemographic information and to have their Internet browsing activitiesmonitored. When an individual joins the panel, they provide detailedinformation concerning their identity and demographics (e.g., gender,race, income, home location, occupation, etc.) to the audiencemeasurement company. The audience measurement entity sets a cookie(e.g., a panelist cookie) on the panelist computer that enables theaudience measurement entity to identify the panelist whenever thepanelist accesses tagged media (e.g., media associated with beacon ortag instructions) and, thus, sends monitoring information to theaudience measurement entity.

Since most of the clients providing monitoring information from thetagged pages are not panelists and, thus, are unknown to the audiencemeasurement entity, it has heretofore been necessary to use statisticalmethods to impute demographic information based on the data collectedfor panelists to the larger population of users providing data for thetagged media. However, panel sizes of audience measurement entitiesremain small compared to the general population of users. Thus, aproblem is presented as to how to increase panel sizes while ensuringthe demographics data of the panel is accurate.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers orregistered users. In exchange for the provision of the service, thesubscribers register with the proprietor. As part of this registration,the subscribers provide detailed demographic information. Examples ofsuch database proprietors include social network providers such asFacebook, Myspace, etc. These database proprietors set cookies on thecomputing device (e.g., computer, cell phone, etc.) of their subscribersto enable the database proprietors to recognize the users when theyvisit their websites.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in the HFZlaw.com domain is accessible toservers in the HFZlaw.com domain, but not to servers outside thatdomain. Therefore, although an audience measurement entity might find itadvantageous to access the cookies set by the database proprietors, theyare unable to do so.

In view of the foregoing, FIGS. 1-15 illustrate and the correspondingportions of the specification describe methods and apparatus to leveragethe existing databases of database proprietors to collect more extensiveInternet usage and demographic data. For example, some of the examplemethods and apparatus leverage cookies stored on a user computer thatidentify a user that has logged into a database provider. However, for ashared computer, the identity of the user logged into the databaseprovider may not match the person that is actually using the computer.Consider a circumstance in which a first user logs into the databaseprovider and the cookie is stored on the computer. Assume the first userstops using the computer but does not log out of the database provider.Further, assume a second user then starts using the computer. If thecookie from the database provider is used to identify the computer userwhile the second user is using the computer, activity at the computerwill wrongly be attributed to the first user. Another problem occurswhen no database provider cookie exists on a computer. When no cookieexists on a computer, techniques that leverage the cookie will not beable to associate demographic information with the computer activity.

To compensate for incorrect attribution due to incorrect prediction of auser during computing activity, example methods and apparatus describedin conjunction with FIGS. 16-28 determine adjustment factor(s) for datadetermined using database providers (e.g., information determined asdescribed in conjunction with FIGS. 1-15). Some panelists (“traditionalpanelists”) are willing to provide their demographic information to ameasurement entity and/or allow collection of more detailed informationabout their computer activity. For example, some traditional panelistsallow panelist meter software to be installed on their computer to trackdetailed activity of computer activity. In some examples disclosedherein, an adjustment factor (e.g., an error rate, a correction factor,compensation factor, etc.) is determined by comparing demographicinformation collected from panelist meter software for computingsessions with demographic information determined using a databaseprovider for the same computing sessions. In other words, demographicinformation for a computing session is determined using two differenttechniques (e.g., prompting a user to identify themselves using panelistmetering software and determining an identify of the user using adatabase provider) and the demographic information for the twotechniques is compared to determine the adjustment factor. The exampleadjustment factor can then be applied to other computing sessions (e.g.,demographic information for all computing sessions (or a subset)determined using the database provider). In some examples, thecomparison and determination of the adjustment factor is determined forall traditional panelists to more accurately determine the adjustmentfactor. An adjustment factor may be determined for each media provider,for each group of media providers, for the entire universe of mediaproviders, and/or any combination of the foregoing.

Turning to the examples of FIGS. 1-15, example methods, apparatus,systems, and/or articles of manufacture disclosed herein cooperate withone or more database proprietors (also referred to herein as partners).The database proprietors provide Internet services to their registeredusers (e.g., users of those database proprietors) and store demographicinformation (e.g., in user account records) for those registered users.As part of this effort, the database proprietor agrees to providedemographic information of its registered users to the audiencemeasurement entity for purposes of measuring demographic-based exposuresto media such as content and/or advertisements. To prevent violatingprivacy agreements with the registered users of the database proprietor,examples disclosed herein employ cookie mapping techniques. That is, thedatabase proprietor can maintain a mapping of its registered usercookies (i.e., partner cookies assigned by the database proprietor toits registered users) to cookies assigned by the audience measuremententity (i.e., audience measurement entity (AME) cookies) to the sameregistered users. In this manner, the audience measurement entity canlog impressions of registered users based on the AME cookies and sendfull or partial AME cookie-based impression logs to a databaseproprietor. The database proprietor can, in turn, match its registeredusers to the AME cookie-based impressions based on its partner-to-AMEcookie map. The database proprietor can then use the matches toassociate demographic information for the matching registered users withcorresponding impression logs. The database proprietor can then removeany identifying data (i.e., partner cookie data) from thedemographic-based impression logs and provide the demographic-basedimpression logs to the audience measurement entity without revealing theidentities of the database proprietor's registered users to the audiencemeasurement entity. Thus, example techniques disclosed herein may beimplemented without compromising privacies of registered users ofdatabase proprietors that partner with an audience measurement entity totrack impressions based on audience demographics.

A database proprietor (e.g., Facebook) can access cookies it has set ona client device (e.g., a computer) to thereby identify the client basedon the internal records (e.g., user account records) of the databaseproprietor. Because the identification of client devices is done withreference to enormous databases of registered users far beyond thequantity of persons present in a typical audience measurement panel,this process may be used to develop data that is extremely accurate,reliable, and detailed.

Because the audience measurement entity remains the first leg of thedata collection process (i.e., receives tag requests generated by taginstructions from client devices to log impressions), the audiencemeasurement entity is able to obscure the source of the media accessbeing logged as well as the identity of the media (e.g., content,webpages, advertisements, and/or other types of media) itself from thedatabase proprietors (thereby protecting the privacy of the mediasources), without compromising the ability of the database proprietorsto provide demographic information corresponding to ones of theirsubscribers for which the audience measurement entity loggedimpressions.

Example methods, apparatus, and/or articles of manufacture disclosedherein can be used to determine impressions or exposures to webpages,advertisements and/or other types of media using demographicinformation, which is distributed across different databases (e.g.,different website owners, different service providers, etc.) on theInternet. Not only do example methods, apparatus, and articles ofmanufacture disclosed herein enable more accurate correlation ofdemographics to media impressions, but they also effectively extendpanel sizes and compositions beyond persons participating (and/orwilling to participate) in the panel of a ratings entity to personsregistered in other Internet databases such as the databases of socialmedia sites such as Facebook, Twitter, Google, etc. This extensioneffectively leverages the media tagging capabilities of the audienceratings entity and the use of databases of non-ratings entities such associal media and other websites to create an enormous, demographicallyaccurate panel that results in accurate, reliable measurements ofexposures to Internet media such as webpages, advertising, content ofany type, and/or programming.

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. That is, anaudience measurement entity enrolls people that consent to beingmonitored into a panel. During enrollment, the audience measuremententity receives demographic information from the enrolling people sothat subsequent correlations may be made between media (e.g., contentand/or advertisements) exposure to those panelists and differentdemographic markets. Unlike traditional techniques in which audiencemeasurement entities rely solely on their own panel member data tocollect demographics-based audience measurements, example methods,apparatus, and/or articles of manufacture disclosed herein enable anaudience measurement entity to obtain demographic information from otherentities that operate based on user registration models. As used herein,a user registration model is a model in which users subscribe toservices of those entities by creating user accounts and providingdemographic-related information about themselves. Obtaining suchdemographic information associated with registered users of databaseproprietors enables an audience measurement entity to extend orsupplement its panel data with substantially reliable demographicsinformation from external sources (e.g., database proprietors), thusextending the coverage, accuracy, and/or completeness of theirdemographics-based audience measurements. Such access also enables theaudience measurement entity to monitor persons who would not otherwisehave joined an audience measurement panel.

Any entity having a database identifying demographics of a set ofindividuals may cooperate with the audience measurement entity. Suchentities are referred to herein as “database proprietors” and includeentities such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes,Experian, etc. Such database proprietors may be, for example, online webservices providers. For example, a database proprietor may be a socialnetwork site (e.g., Facebook, Twitter, MySpace, etc.), a multi-servicesite (e.g., Yahoo!, Google, Experian, etc.), an online retailer site(e.g., Amazon.com, Buy.com, etc.), and/or any other web services sitethat maintains user registration records and irrespective of whether thesite fits into none, or one or more of the categories noted above.

Example methods, apparatus, and/or articles of manufacture disclosedherein may be implemented by an audience measurement entity, a ratingsentity, and/or any other entity interested in measuring or trackingaudience exposures to content, advertisements and/or any other type(s)of media.

To increase the likelihood that measured viewership is accuratelyattributed to the correct demographics, example methods, apparatus,and/or articles of manufacture disclosed herein use demographicinformation located in the audience measurement entity's records as wellas demographic information located at one or more database proprietors(e.g., web service providers) that maintain records or profiles of usershaving accounts therewith. In this manner, example methods, apparatus,and/or articles of manufacture may be used to supplement demographicinformation maintained by a ratings entity (e.g., an audiencemeasurement company such as The Nielsen Company of Schaumburg, Ill.,United States of America, that collects media exposure measurementsand/or demographics) with demographic information from one or moredifferent database proprietors (e.g., web service providers).

The use of demographic information from disparate data sources (e.g.,high-quality demographic information from the panels of an audiencemeasurement company and/or registered user data of web serviceproviders) results in, for example, improving the reportingeffectiveness of metrics for online and/or offline advertisingcampaigns. Examples disclosed herein use online registration data toidentify demographics of users. Such examples also use server impressioncounts, tagging (also referred to as beaconing), and/or other techniquesto track quantities of advertisement and/or media impressionsattributable to those users. Online web service providers such as socialnetworking sites and multi-service providers (collectively andindividually referred to herein as online database proprietors) maintaindetailed demographic information (e.g., age, gender, geographiclocation, race, income level, education level, religion, etc.) collectedvia user registration processes. An impression corresponds to a home orindividual having been exposed to the corresponding media (e.g., contentand/or advertisement). Thus, an impression represents a home or anindividual having been exposed to an advertisement and/or content orgroup of advertisements or content. In Internet advertising, a quantityof impressions or impression count is the total number of times anadvertisement or advertisement campaign has been accessed by a webpopulation (e.g., including number of times accessed as decreased by,for example, pop-up blockers and/or increased by, for example, retrievalfrom local cache memory).

Example impression reports generated using example methods, apparatus,and/or articles of manufacture disclosed herein may be used to report TVGRPs and online GRPs in a side-by-side manner. For instance, advertisersmay use impression reports to report quantities of unique people orusers that are reached individually and/or collectively by TV and/oronline advertisements.

Although examples are disclosed herein in connection withadvertisements, advertisement exposures, and/or advertisementimpressions, such examples may additionally or alternatively beimplemented in connection with other types of media in addition to orinstead of advertisements. That is, processes, apparatus, systems,operations, structures, data, and/or information disclosed herein inconnection with advertisements may be similarly used and/or implementedfor use with other types of media such as content. As used herein,“media” refers to content (e.g., websites, movies, television and/orother programming) and/or advertisements.

Turning now to FIG. 1, an example system 100 is shown. In theillustrated example, the system 100 includes an impression monitorsystem 102 which may be owned and/or operated by an audience measuremententity 103. In the illustrated examples, the impression monitor system102 works cooperatively with one or more database proprietors, two ofwhich are shown as a partner A database proprietor 104 a and a partner Bdatabase proprietor 104 b, to generate impression reports 106 a and 106b using distributed demographic information collected by the databaseproprietors 104 a and 104 b. In the illustrated example, the impressionreports 106 a and 106 b are indicative of demographic segments,populations, or groups that were exposed to identified advertisements orcontent. “Distributed demographics information” is used herein to referto demographics information obtained from a database proprietor such asan online web services provider. In the illustrated example, theimpression monitor system 102 may be owned and/or operated by anaudience measurement entity to collect and log impressions from clientdevices 108 using, for example, audience measurement entity (AME)cookies set on those client devices 108. In illustrated examplesdescribed herein, AME cookies (e.g., an AME cookie 208 of FIG. 2) areset in the client devices 108 in response to contacting the audiencemeasurement entity 103 after executing monitoring or tag instructionsregardless of whether all, some, or none of the client devices 108 areassociated with audience member panels of the audience measuremententity 103. That is, by setting AME cookies in the client devices 108,the audience measurement entity 103 is able to log ad and/or mediaimpressions regardless of whether the ad and/or media impressions areattributable to panelists or non-panelists. In the illustrated exampleof FIG. 1, the client devices 108 may be stationary or portablecomputers, handheld computing devices (e.g., tablets such as iPads®),smart phones, Internet appliances, and/or any other type of device thatmay be connected to the Internet and capable of presenting media.

In the illustrated example, media providers and/or advertisersdistribute advertisements 110 via the Internet to users that accesswebsites and/or online television services (e.g., web-based TV, Internetprotocol TV (IPTV), etc.). In the illustrated example, theadvertisements 110 may be individual, stand alone ads and/or may be partof one or more ad campaigns. The ads of the illustrated example areencoded with identification codes (i.e., data) that identify theassociated ad campaign (e.g., campaign ID, if any), a creative type ID(e.g., identifying a Flash-based ad, a banner ad, a rich type ad, etc.),a source ID (e.g., identifying the ad publisher), and/or a placement ID(e.g., identifying the physical placement of the ad on a screen). Theadvertisements 110 of the illustrated example are also tagged or encodedto include computer executable monitoring instructions (e.g., Java, javascript, or any other computer language or script) that are executed byweb browsers that access the advertisements 110 via, for example, theInternet. In the illustrated example of FIG. 1, the advertisements 110are presented to audience members via the client devices 108. Computerexecutable monitoring instructions may additionally or alternatively beassociated with media to be monitored. Thus, although this disclosurefrequently speaks in terms of tracking advertisements, it is notrestricted to tracking any particular type of media. On the contrary, itcan be used to track media (e.g., content and/or advertisements) of anytype or form in a network. Irrespective of the type of media beingtracked, execution of the monitoring instructions causes the web browserto send impression request(s) 112 (e.g., referred to herein as tagrequests 112) to a specified server (e.g., the audience measuremententity). The tag request(s) 112 may be implemented using HTTP requests.However, whereas HTTP requests traditionally identify web pages or otherresources to be downloaded, the tag request(s) 112 of the illustratedexample include audience measurement information (e.g., ad campaignidentification, media identifier, content identifier, and/or useridentification information) as their payloads. The server (e.g., theimpression monitor system 102) to which the tag request(s) 112 aredirected is programmed to log the audience measurement data caused bythe tag request(s) 112 as impressions (e.g., ad and/or media impressionsdepending on the nature of the media tagged with the monitoringinstructions). To collect and log exposure measurements, the impressionmonitor system 102 includes an AME impressions store 114. Exampleimpression logging processes are described in detail below in connectionwith FIG. 3.

In some examples, advertisements tagged with such tag instructions aredistributed with Internet-based media such as, for example, web pages,streaming video, streaming audio, IPTV content, etc. As noted above,methods, apparatus, systems, and/or articles of manufacture disclosedherein are not limited to advertisement monitoring but can be adapted toany type of content monitoring (e.g., web pages, movies, televisionprograms, etc.) Example techniques that may be used to implement suchmonitoring, tag and/or beacon instructions are described in Blumenau,U.S. Pat. No. 6,108,637, which is hereby incorporated herein byreference in its entirety.

In the illustrated example of FIG. 1, the impression monitor system 102tracks users associated with impressions using AME cookies (e.g.,name-value pairs of Universally Unique Identifiers (UUIDs)) when theclient devices 108 present tagged advertisements (e.g., theadvertisements 110) and/or other tagged media. Due to Internet securityprotocols, the impression monitor system 102 can only collect cookiesset in its domain (e.g., AME cookies). Thus, if, for example, theimpression monitor system 102 operates in the “Nielsen.com” domain, itcan only collect cookies set in the Nielsen.com domain. Thus, when theimpression monitor system 102 receives tag request(s) 112 from theclient devices 108, the impression monitor system 102 only has access toAME cookies set on that client device for, for example, the Nielsen.comdomain, but not cookies set outside its domain (e.g., outside theNielsen.com domain).

To overcome the domain limitations associated with collecting cookieinformation, the impression monitoring system 102 monitors impressionsof users of the client devices 108 that are registered users of one orboth of the partner A and partner B database proprietors 104 a and 104b. When a user of one of the client devices 108 logs into a service ofone of the database proprietors 104 a or 104 b, the client device 108 isdirected to the impression monitor system 102 to perform aninitialization (IN IT) AME cookie message exchange 116 with theimpression monitor system 102 and sends a login reporting message 118 tothe database proprietor providing that service. For example, asdescribed in more detail below in connection with FIG. 2, if a user logsinto a service of the partner A database proprietor 104 a, the INIT AMEcookie message exchange 116 sets an AME cookie in the client device 108based on the domain of the impression monitor system 102 for the userthat logged into the service of the partner A database proprietor 104 a.In addition, the login reporting message 118 sent to the partner Adatabase proprietor 104 a includes the same AME cookie for the clientdevice 108 and a partner A cookie set by the partner A databaseproprietor 104 a for the same client device 108. In the illustratedexample, the partner A database proprietor 104 a sets the partner Acookie in the client device 108 when the client device 108 visits awebpage of the partner A database proprietor 104 a and/or when a userlogs into a service of the partner A database proprietor 104 a via alogin page of the partner A database proprietor 104 a (e.g., the loginwebpage 204 of FIG. 2). In the illustrated example, the AME cookie isoutside a domain (e.g., a root domain) of the partner A cookie. Becausethe login reporting message 118 includes the AME cookie, it enables thepartner A database proprietor 104 a to map its partner A cookie to theAME cookie for the user of the client device 108. The INIT AME cookiemessage exchange 116 includes a login timestamp indicative of when auser associated with the specified AME cookie logged into the partner Adatabase proprietor 104 a. If an AME cookie was previously set for theclient, a new AME cookie is not set unless the previous AME cookie hasbeen removed from the client, is no longer present on the client, and/orhas expired. These processes are described in greater detail below inconnection with FIG. 2.

Subsequently, the impression monitor system 102 receives the tagrequest(s) 112 based on ads and/or content presented via the clientdevices 108 and logs impressions based on the presented ads and/orcontent in association with respective AME cookies of the client devices108 as described in detail below in connection with FIG. 3. In theillustrated example of FIG. 1, the impression monitor system 102 storesthe logged impressions in the AME impressions store 114 and subsequentlysends AME impression logs 122 containing some or all of the loggedimpressions from the AME impressions store 114 to the partner databaseproprietors 104 a and 104 b.

Each of the partner database proprietors 104 a-b may subsequently usetheir respective AME cookie-to-partner cookie mappings to matchdemographics of users of the client devices 108 identified based onpartner cookies with impressions logged based on AME cookies in the AMEimpression logs 122. Example demographic matching and reporting isdescribed in greater detail below in connection with FIG. 4. Because theaudience measurement entity 103 sets AME cookies on any client thatsends it a tag request (i.e., including non-panelists), the map of theAME cookies to partner cookies is not limited to panelists but insteadextends to any client that accesses tagged media. As a result, theaudience measurement entity 103 is able to leverage the data of thepartner as if the non-panelists with AME cookies were panelists of theaudience measurement entity 103, thereby effectively increasing thepanel size. In some examples, the panel of the audience measuremententity is eliminated.

FIG. 2 depicts an example manner of setting cookies with the impressionmonitor system 102 and reporting the same to the database proprietors(e.g., the partner A database proprietor 104 a and/or the partner Bdatabase proprietor 104 b) in response to users logging in to websitesof the database proprietors. One of the client devices 108 of FIG. 1 isshown in FIG. 2 and is provided with a cookie reporter 202 configured tomonitor login events on the client device 108 and to send cookies to theimpression monitor system 102 and the database proprietors 104 a and 104b. In the illustrated example of FIG. 2, the cookie reporter 202 isshown performing the INIT AME cookie message exchange 116 with theimpression monitor system 102 and sending the login reporting message118 to the partner A database proprietor 104 a.

In the illustrated example of FIG. 2, the cookie reporter 202 isimplemented using computer executable instructions (e.g., Java, javascript, or any other computer language or script) that are executed byweb browsers. Also in the illustrated example of FIG. 2, the cookiereporter 202 is provided to the clients, directly or indirectly, by anaudience measurement entity that owns and/or operates the impressionmonitor system 102. For example, the cookie reporter 202 may be providedto the database proprietor from the AME 103 and subsequently downloadedto the client device 108 from a server serving a login webpage 204 ofthe partner A database proprietor 104 a (or of the partner B databaseproprietor 104 b or of any other partner database proprietor) inresponse to the client device 108 requesting the login webpage.

A web browser of the client device 108 may execute the cookie reporter202 to monitor for login events associated with the login page 204. Whena user logs in to a service of the partner A database proprietor 104 avia the login page 204, the cookie reporter 202 initiates the INIT AMEmessage exchange 116 by sending a request 206 to the impression monitorsystem 102. In the illustrated example of FIG. 2, the request 206 is adummy request because its purpose is not to actually retrieve a webpage,but is instead to cause the impression monitor system 102 to generate anAME cookie 208 for the client device 108 (assuming an AME cookie has notalready been set for and/or is not present on the client). The AMEcookie 208 uniquely identifies the client device 108. However, becausethe client device 108 may not be associated with a panelist of the AME103, the identity and/or characteristics of the user may not be known.The impression monitor system 102 subsequently uses the AME cookie 208to track or log impressions associated with the client device 108,irrespective of whether the client device 108 is a panelist of the AME103, as described below in connection with FIG. 3. Because disclosedexamples monitor clients as panelists even though they may not have beenregistered (i.e., have not agreed to be a panelist of the AME 103), suchclients may be referred to herein as pseudo-panelists.

The request 206 of the illustrated example is implemented using an HTTPrequest that includes a header field 210, a cookie field 212, and apayload field 214. The header field 210 stores standard protocolinformation associated with HTTP requests. When the client device 108does not yet have an AME cookie set therein, the cookie field 212 isempty to indicate to the impression monitor system 102 that it needs tocreate and set the AME cookie 208 in the client device 108. In responseto receiving a request 206 that does not contain an AME cookie 208, theimpression monitor system 102 generates an AME cookie 208 and sends theAME cookie 208 to the client device 108 in a cookie field 218 of aresponse message 216 as part of the INIT AME cookie message exchange 116of FIG. 1 to thereby set the AME cookie 208 in the client device 108.

In the illustrated example of FIG. 2, the impression monitor system 102also generates a login timestamp 220 indicative of a time at which auser logged in to the login page 204 and sends the login timestamp 220to the client device 208 in a payload field 222 of the response 216. Inthe illustrated example, the login timestamp 220 is generated by theimpression monitor system 102 (e.g., rather than the client device 108)so that all login events from all client devices 108 are time stampedbased on the same clock (e.g., a clock of the impression monitor system102). In this manner, login times are not skewed or offset based onclocks of respective client devices 108, which may have differences intime between one another. In some examples, the timestamp 220 may beomitted from the payload 222 of the response 216, and the impressionmonitor system 102 may instead indicate a login time based on atimestamp in a header field 224 of the response 216. In some examples,the response 216 is an HTTP 302 redirect response which includes a URL226 of the partner A database proprietor 104 a to which the cookiereporter 202 should send the AME cookie 208. The impression monitorsystem 102 populates the redirect response with the URL.

In the illustrated example of FIG. 2, after receiving the response 216,the cookie reporter 202 generates and sends the login reporting message118 to the partner A database proprietor 104 a. For example, the cookiereporter 202 of the illustrated example sends the login reportingmessage 118 to a URL indicated by the login page 204. Alternatively, ifthe response 216 is an HTTP 302 redirect and includes the URL 226, thecookie reporter 202 sends the login reporting message 118 to the partnerA database proprietor 104 a using the URL 226. In the illustratedexample of FIG. 2, the login reporting message 118 includes a partner Acookie 228 in a cookie field 230. The partner A cookie 228 uniquelyidentifies the client device 108 for the partner A database proprietor104 a. Also in the illustrated example, the cookie reporter 202 sendsthe AME cookie 208 and the login timestamp 220 in a payload field 232 ofthe login reporting message 118. Thus, in the illustrated example ofFIG. 2, the AME cookie 208 is sent as regular data (e.g., a dataparameter) or payload in the login reporting message 118 to the partnerA database proprietor 104 a to overcome the fact that the AME cookie 208was not set in the domain of the partner A database proprietor 104 a,and, thus, could not be sent to a third party as an ordinary cookie. Inthe illustrated example, the AME cookie 208 corresponds to anotherdomain (e.g., a Nielsen.com root domain) outside the domain of thepartner A cookie 228 (e.g., a Facebook.com root domain). Using exampleprocesses illustrated in FIG. 2 advantageously enables sending cookiedata across different domains, which would otherwise not be possibleusing known cookie communication techniques. The database proprietor 104a receives the AME cookie 208 in association with the partner A cookie228, thereby, creating an entry in an AME cookie-to-partner cookie map(e.g., the partner cookie map 236).

Although the login reporting message 118 is shown in the example of FIG.2 as including the partner A cookie 228, for instances in which thepartner A database proprietor 104 a has not yet set the partner A cookie228 in the client device 108, the cookie field 230 is empty in the loginreporting message 118. In this manner, the empty cookie field 230prompts the partner A database proprietor 104 a to set the partner Acookie 228 in the client device 108. In such instances, the partner Adatabase proprietor 104 a sends the client device 108 a response message(not shown) including the partner A cookie 228 and records the partner Acookie 228 in association with the AME cookie 208.

In some examples, the partner A database proprietor 104 a uses thepartner A cookie 228 to track online activity of its registered users.For example, the partner A database proprietor 104 a may track uservisits to web pages hosted by the partner A database proprietor 104 a,display those web pages according to the preferences of the users, etc.The partner A cookie 228 may also be used to collect “domain-specific”user activity. As used herein, “domain-specific” user activity is userInternet activity associated within the domain(s) of a single entity.Domain-specific user activity may also be referred to as “intra-domainactivity.” In some examples, the partner A database proprietor 104 acollects intra-domain activity such as the number of web pages (e.g.,web pages of the social network domain such as other social networkmember pages or other intra-domain pages) visited by each registereduser and/or the types of devices such as mobile devices (e.g., smartphones, tablets, etc.) or stationary devices (e.g., desktop computers)used for access. The partner A database proprietor 104 a may also trackaccount characteristics such as the quantity of social connections(e.g., friends) maintained by each registered user, the quantity ofpictures posted by each registered user, the quantity of messages sentor received by each registered user, and/or any other characteristic ofuser accounts.

In some examples, the cookie reporter 202 is configured to send therequest 206 to the impression monitor system 102 and send the loginreporting message 118 to the partner A database proprietor 104 a onlyafter the partner A database proprietor 104 a has indicated that a userlogin via the login page 204 was successful. In this manner, the request206 and the login reporting message 118 are not performed unnecessarilyshould a login be unsuccessful. In the illustrated example of FIG. 2, asuccessful login ensures that the partner A database proprietor 104 awill associate the correct demographics of a logged in registered userwith the partner A cookie 228 and the AME cookie 208.

In the illustrated example of FIG. 2, the partner A database proprietor104 a includes a server 234, a partner cookie map 236, and a useraccounts database 238. Although not shown, other database proprietors(e.g., the partner B database proprietor 104 b of FIG. 1) that partnerwith the audience measurement entity 103 (FIG. 1) also include arespective partner cookie map similar to the partner cookie map 236 anda user accounts database similar to the user accounts database 238 but,of course, relative to their own users. The server 234 of theillustrated example communicates with the client device 108 to, forexample, receive login information, receive cookies from the clientdevice 108, set cookies in the client device 108, etc.

The partner cookie map 236 stores partner cookies (e.g., the partner Acookie 228) in association with respective AME cookies (e.g., the AMEcookie 208) and respective timestamps (e.g., the timestamp 220). In theillustrated example of FIG. 2, the partner cookie map 236 stores aunique user ID (UUID) found in a name-value pair (i.e., a parameter namesuch as ‘user_ID’ and a value such as the UUID) of the partner A cookie228 in association with a unique user ID found in a name-value pair ofthe AME cookie 208. In addition, the partner cookie map 236 stores thelogin timestamp 220 in association with the UUIDs to indicate a time atwhich a corresponding user login occurred. Referring briefly to FIG. 5,an example implementation of the partner cookie map 236 is shown, inwhich an AME user ID column 502 stores UUIDs from AME cookies (e.g., theAME cookie 208 of FIG. 2), a partner user ID column 504 stores UUIDsfrom partner cookies (e.g., the partner A cookie 228 of FIG. 2), and alogin timestamp column 506 stores login timestamps (e.g., the logintimestamp 220 of FIG. 2). In illustrated examples disclosed herein, thepartner A database proprietor 104 a uses the partner cookie map 236 tomatch impressions received from the impression monitor system 102 basedon AME cookies (e.g., the AME cookie 208) to registered users of thepartner A database proprietor 104 a identified by respective partner Acookies (e.g., the partner A cookie 228). In this manner, the partner Adatabase proprietor 104 a can determine which of its registered usersare associated with specific impressions logged by the impressionmonitor system 102.

Returning to FIG. 2, the partner A database proprietor 104 a uses theuser accounts database 238 to store, among other things, demographicinformation for registered users of the partner A database proprietor104 a. In the illustrated example of FIG. 2, such demographicinformation is received from registered users during an enrollmentand/or registration process or during a subsequent personal informationupdate process. The demographic information stored in the user accountsdatabase 238 may include, for example, age, gender, interests (e.g.,music interests, movie interests, product interests, or interestsassociated with any other topic), number of friends or socialconnections maintained by each registered user via the partner Adatabase proprietor 104 a, personal yearly income, household income,geographic location of residence, geographic location of work,graduation year(s), quantity of group associations, or any otherdemographic information. The partner A database proprietor 104 a usesthe user accounts database 238 to associate demographic information toparticular impressions logged by the impression monitor system 102 afterdetermining which registered users of the partner A database proprietor104 a correspond to which logged impressions based on the partner cookiemap 236.

FIG. 3 depicts an example system 300 that may be used to log impressionsat the impression monitor system 102 of the example system 100 ofFIG. 1. The example system 300 enables the impressions monitor system102 of FIGS. 1 and 2 to log impressions in association withcorresponding AME cookies (e.g., the AME cookie 208 of FIG. 2) based ontag requests (e.g., the tag request(s) 112 of FIG. 1) received from aweb browser 302 executed by a client device (e.g., any client device 108of FIGS. 1 and 2). In the illustrated example of FIG. 3, the impressionmonitor system 102 logs impressions from any client device (e.g., theclient devices 108 of FIG. 1) from which it receives a tag request 112as described below. The impression monitor system 102 compiles thereceived impression data in the AME impression data store 114.

Turning in detail to FIG. 3, the client device may be any one of theclient devices 108 of FIGS. 1 and 2 or another device not shown in FIG.1 or 2. However, for simplicity of discussion and without loss ofgenerality, the client device will be referred to as client device 108.As shown, the client device 108 sends communications to the impressionsmonitor system 102. In the illustrated example, the client device 108executes the web browser 302, which is directed to a host website (e.g.,www.acme.com) that displays one of the advertisement(s) 110 receivedfrom an ad publisher 303. The advertisement 110 of the illustratedexample is tagged with identifier information (e.g., a campaign ID, acreative type ID, a placement ID, a publisher source URL, etc.) and taginstructions 304. When the tag instructions 304 are executed by theclient device 108, the tag instructions 304 cause the client device 108to send a tag request 112 to a URL address of the impressions monitorsystem 102 as specified in the tag instructions 304. Alternatively, theURL address specified in the tag instructions 304 may direct the tagrequest 112 to any other server owned, operated, and/or accessible bythe audience measurement entity 103 (FIG. 1) or another entity. The taginstructions 304 may be implemented using java script or any othertype(s) of executable instruction(s) including, for example, Java, HTML,etc. It should be noted that tagged content such as web pages, and/orany other media are processed the same way as the tagged advertisement110. That is, for any tagged media, corresponding tag instructions arereceived in connection with the download of the tagged content and causea tag request to be sent from the client device that downloaded thetagged content to the impression monitor system 102 (or any other serverindicated by the instructions).

In the illustrated example of FIG. 3, the tag request 112 is implementedusing an HTTP request and is shown in detail as including a header field310, a cookie field 312, and a payload field 314. In the illustratedexample of FIG. 3, the web browser 302 stores the AME cookie 208 of FIG.2 in the cookie field 312 and stores ad campaign information 316 and apublisher site ID 318 in the payload field 314. In the illustratedexample, the ad campaign information 316 may include informationidentifying one or more of an associated ad campaign (e.g., an adcampaign ID), a creative type ID (e.g., identifying a Flash-based ad, abanner ad, a rich type ad, etc.), and/or a placement ID (e.g.,identifying the physical placement of the ad on a screen). In someexamples, to log a media impression, the ad campaign information 316 isreplaced with media information identifying the media (e.g., a mediaidentifier), a creative ID, and/or a placement ID. In the illustratedexample, the publisher site ID 318 identifies a source of theadvertisement 110 and/or content (e.g., a source ID identifying the adpublisher 303 and/or media publisher).

In the illustrated example, in response to receiving the tag request112, the impression monitor system 102 logs an impression associatedwith the client device 108 in the AME impressions store 114 by storingthe AME cookie 208 in association with a media identifier (e.g., the adcampaign information 316 and/or the publisher site ID 318). In addition,the impression monitor system 102 generates a timestamp indicative ofthe time/date of when the impression occurred and stores the timestampin association with the logged impression. An example implementation ofthe example AME impression store 114 is shown in FIG. 6. Turning brieflyto FIG. 6, the AME impression store 114 includes an AME user ID column602 to store AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3), atimestamp column 604 to store impression timestamps indicative of whenimpressions occurred at client devices (e.g., the client device 108 ofFIGS. 1-3), a campaign ID column 606 to store the campaign information316 of FIG. 3, and a site ID column 608 to store the publisher site ID318 of FIG. 3.

FIG. 4 is an example apparatus 400 that may be used to associateimpressions with demographics of users (e.g., users of the clientdevices 108 of FIGS. 1-3) registered with one or more databaseproprietors (e.g., the partner database proprietors 104 a-b of FIGS.1-3). In some examples, the apparatus 400 is implemented at one or moredatabase proprietors (e.g., the partner database proprietors 104 a-b ofFIGS. 1-3). Alternatively, the apparatus 400 may be implemented at othersites. In some examples, the apparatus 400 may be developed by theaudience measurement entity 103 (FIG. 1) and provided to a databaseproprietor to enable the database proprietor to combine databaseproprietor-owned demographic information with impression logs providedby the audience measurement entity 103. To ensure privacy of registeredusers of a database proprietor, the audience measurement entity 103 mayinstall or locate the example apparatus 400 at a database proprietor sothat the database proprietor need not provide identities of itsregistered users to the audience measurement entity 103 in order toassociate demographics information with logged impressions. Instead, theaudience measurement entity 103 can provide its logged impressions(e.g., the AME impression logs 122) to the database proprietor and thedatabase proprietor can associate respective demographics with thelogged impressions while concealing the identities (e.g., names andcontact information) of its registered users.

In the illustrated example, the apparatus 400 is provided with anexample cookie matcher 402, an example demographics associator 404, anexample demographics analyzer 406, an example demographics modifier 408,an example user ID modifier 410, an example report generator 412, anexample data parser 414, an example mapper 416, and an exampleinstructions interface 418. While an example manner of implementing theapparatus 400 has been illustrated in FIG. 4, one or more of theelements, processes and/or devices illustrated in FIG. 4 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the cookie matcher 402, the demographicsassociator 404, the demographics analyzer 406, the demographics modifier408, the user ID modifier 410, the report generator 412, the data parser414, the mapper 416, the instructions interface 418 and/or, moregenerally, the example apparatus 400 of FIG. 4 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the cookie matcher402, the demographics associator 404, the demographics analyzer 406, thedemographics modifier 408, the user ID modifier 410, the reportgenerator 412, the data parser 414, the mapper 416, the instructionsinterface 418 and/or, more generally, the example apparatus 400 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),etc. When any of the apparatus or system claims of this patent are readto cover a purely software and/or firmware implementation, at least oneof the cookie matcher 402, the demographics associator 404, thedemographics analyzer 406, the demographics modifier 408, the user IDmodifier 410, the report generator 412, the data parser 414, the mapper416, and/or the instructions interface 418 are hereby expressly definedto include a tangible computer readable medium such as a memory, DVD,CD, BluRay disk, etc. storing the software and/or firmware. Furtherstill, the example apparatus 400 of FIG. 4 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

Turning in detail to FIG. 4, in the illustrated example, the apparatus400 is implemented at the partner A database proprietor 104 a (FIGS. 1and 2). Other instances of the apparatus 400 could be similarlyimplemented at any other database proprietor participating with the AME103 (e.g., the partner B database proprietor 104 b). In the illustratedexample of FIG. 4, the apparatus 400 receives the AME impression logs122 from the impression monitor system 102 to enable the apparatus 400to associate user/audience member demographics from the user accountsdatabase 238 with logged impressions.

In the illustrated example, the apparatus 400 is provided with thecookie matcher 402 to match AME user IDs from AME cookies (e.g., the AMEcookie 208 of FIGS. 2 and 3) from the AME impression logs 122 to AMEuser IDs in the partner A cookie map 236. The apparatus 400 performssuch cookie matching to identify registered users of the partner Adatabase proprietor 104 a to which the logged impressions areattributable (e.g., partner A registered users for which the impressionmonitor system 102 set AME cookies as described above in connection withFIG. 2 and tracked impressions as described above in connection withFIG. 3). For example, the partner cookie map 236 is shown in FIG. 5 asassociating AME user IDs in the AME user ID column 502 to partner userIDs in the partner user ID column 504. The AME impression logs 122 arestructured similar to the data in the AME impression store 114 as shownin FIG. 6, which logs impressions per AME user ID. Thus, the cookiematcher 402 matches AME user IDs from the AME user ID column 602 of theAME impression logs 122 to AME user IDs of the AME user ID column 502 ofthe partner cookie map 236 to associate a logged impression from the AMEimpression logs 122 to a corresponding partner user ID mapped in thepartner cookie map 236 of FIG. 5. In some examples, the AME 103encrypts, obfuscates, varies, etc. campaign IDs in the AME impressionlogs 122 before sending the AME impression logs 122 to partner databaseproprietors (e.g., the partner database proprietors 104 a and 104 b ofFIGS. 1 and 2) to prevent the partner database proprietors fromrecognizing the media to which the campaign IDs correspond or tootherwise protect the identity of the media. In such examples, a lookuptable of campaign ID information may be stored at the impression monitorsystem 102 so that impression reports (e.g., the impression reports 106a and 106 b of FIG. 1) received from the partner database proprietorscan be correlated with the media.

In some examples, the cookie matcher 402 uses login timestamps (e.g.,the login timestamp 220 of FIG. 2) stored in the login timestamp column506 of FIG. 5 and impression timestamps stored in the timestamp column604 of FIG. 6 to discern between different users to which impressionslogged by the impression monitor system 102 are attributable. That is,if two users having respective username/password login credentials forthe partner A database proprietor 104 a use the same client device 108,all impressions logged by the impression monitor system 102 will bebased on the same AME cookie (e.g., the AME cookie 208 of FIGS. 2 and 3)set in the client device 108 regardless of which user was using theclient device 108 when the impression occurred. However, by comparinglogged impression timestamps (e.g., in the timestamp column 604 of FIG.6) to login timestamps (e.g., in the login timestamp column 506 of FIG.5), the cookie matcher 402 can determine which user was logged into thepartner A database proprietor 104 a when a corresponding impressionoccurred. For example, if a user ‘TOM’ logged in to the partner Adatabase proprietor 104 a at 12:57 AM on Jan. 1, 2010 and a user ‘MARY’logged in to the partner A database proprietor 104 a at 3:00 PM on Jan.1, 2010 using the same client device 108, the login events areassociated with the same AME cookie (e.g., the AME cookie 208 of FIGS. 2and 3). In such an example, the cookie matcher 402 associates anyimpressions logged by the impression monitor system 102 for the same AMEcookie between 12:57 AM and 3:00 pm on Jan. 1, 2010 to the user TOM′ andassociates any impressions logged by the impression monitor system 102for the same AME cookie after 3:00 pm on Jan. 1, 2010 to the user‘MARY’. Such time-based associations are shown in the illustratedexample data structure of FIG. 7 described below.

In the illustrated example, the cookie matcher 402 compiles the matchedresults into an example partner-based impressions data structure 700,which is shown in detail in FIG. 7. Turning briefly to FIG. 7, thepartner-based impressions structure 700 includes an AME user ID column702, an impression timestamp column 704, a campaign ID column 706, asite ID column 708, a user login timestamp 710, and a partner user IDcolumn 712. In the AME user ID column 702, the cookie matcher 402 storesAME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3). In theimpression timestamp column 704, the cookie matcher 402 storestimestamps generated by the impression monitor system 102 indicative ofwhen each impression was logged. In the campaign ID column 706, thecookie matcher 402 stores ad campaign IDs stored in, for example, thecampaign information 316 of FIG. 3. In some examples, instead of or inaddition to the campaign ID column 706, the partner-based impressionsdata structure 700 includes a content ID column to store identifyinginformation of media. In some examples, some media (e.g.,advertisements, content, and/or other media) is not associated with acampaign ID or content ID. In the illustrated example of FIG. 7, blanksin the campaign ID column 706 indicate media that is not associated withcampaign IDs and/or content IDs. In the site ID column 708, the cookiematcher 402 stores advertisement publisher site IDs (e.g., the publishersite ID 318 of FIG. 3). In the user login timestamp column 710, thecookie matcher 402 stores timestamps (e.g., the timestamp 220 of FIG. 2)indicative of when respective users logged in via partner login pages(e.g., the login page 204 of FIG. 2). In the partner user ID column 712,the cookie matcher 402 stores partner cookies (e.g., the partner Acookie 228 of FIG. 2).

Returning to FIG. 4, in the illustrated example, the apparatus 400 isprovided with the demographics associator 404 to associate demographicsinformation from the user accounts database 238 with correspondingpartner-based impressions from the partner-based impressions structure700. For example, the demographics associator 404 may retrievedemographics information for partner user IDs noted in the partner userID column 712 (FIG. 7) and associate the retrieved demographicsinformation with corresponding ones of the records in the partner-basedimpressions structure 700.

In the illustrated example of FIG. 4, to analyze demographic informationfor accuracy and/or completeness, the apparatus 400 is provided with thedemographics analyzer 406. In addition, to update, modify, and/orfill-in demographics information in inaccurate and/or incompleterecords, the apparatus 400 is provided with the demographics modifier408. In some examples, the demographics analyzer 406 and/or thedemographics modifier 408 analyze and/or adjust inaccurate demographicinformation using example methods, systems, apparatus, and/or articlesof manufacture disclosed in U.S. patent application Ser. No. 13/209,292,filed on Aug. 12, 2011, and titled “Methods and Apparatus to Analyze andAdjust Demographic Information,” which is hereby incorporated herein byreference in its entirety.

In the illustrated example, to remove user IDs from the partner-basedimpressions structure 700 after adding the demographics information andbefore providing the data to the AME 103, the apparatus 400 of theillustrated example is provided with a user ID modifier 410. In theillustrated example, the user ID modifier 410 is configured to at leastremove partner user IDs (from the partner user ID column 712) to protectthe privacy of registered users of the partner A database proprietor 104a. In some examples, the user ID modifier 410 may also remove the AMEuser IDs (e.g., from the AME user ID column 702) so that the impressionreports 106 a generated by the apparatus 400 are demographic-levelimpression reports. “Removal” of user IDs (e.g., by the user ID modifier410 and/or by the report generator 412) may be done by not providing acopy of the data in the corresponding user ID fields as opposed todeleting any data from those fields. If the AME user IDs are preservedin the impressions data structure 700, the apparatus 400 of theillustrated example can generate user-level impression reports.

In the illustrated example of FIG. 4, to generate the impression reports106 a, the apparatus 400 is provided with the report generator 412.Example information that the report generator 412 may generate for theimpression reports 106 a is described in detail below in connection withFIGS. 8 and 9.

In the illustrated example of FIG. 4, to parse information, theapparatus 400 is provided with the data parser 414. In some examples,the data parser 414 receives messages from client devices and/or othersystems and parses information from those received messages. Forexample, the apparatus 400 may use the data parser 414 to receive thelogin reporting message 118 from the cookie reporter 202 (FIG. 2) andparse out the partner A cookie 228, the AME cookie 208, and/or the logintimestamp 220 from the login reporting message 118. In some examples,the apparatus 400 also uses the data parser 414 to parse information inthe AME impression logs 122 and/or to parse information from any otherdata structure and/or message.

In the illustrated example of FIG. 4, to map information, the apparatus400 is provided with the mapper 416. In some examples, the mapper 416maps cookie identifiers associated with the same user but correspondingto different Internet domains. For example, the apparatus 400 may usethe mapper 416 to map the partner A cookie 228 to the AME cookie 208(FIG. 2) in the partner cookie map 236 (FIGS. 2, 4, and 5). In someexamples, the mapper 416 also maps login timestamps with correspondingcookie identifiers. For example, the apparatus 400 may use the mapper416 to map the login timestamp 220 (FIG. 2) with the correspondingpartner A cookie 228 and AME cookie 208 in the partner cookie map 236.

In the illustrated example of FIG. 4, to send computer executableinstructions to the client device(s) 108 to monitor user logins vialogin webpages (e.g., the login webpage 204 of FIG. 2), the apparatus400 is provided with the instructions interface 418. For example, theapparatus 400 may use the instructions interface 418 to send computerexecutable instructions (e.g., Java, java script, or any other computerlanguage or script) to the client device 108 that are executed by theweb browser 302 (FIG. 3) to implement the cookie reporter 202 (FIG. 2).In some examples, the instructions interface 418 sends the computerexecutable instructions to the client device 108 in response toreceiving a request from the web browser 302 for a login webpage (e.g.,the login webpage 204) of an Internet-based service provided by theentity (e.g., one of the database proprietor partners 104 a and 104 b)that implements the apparatus 400. In this manner, the client device 108can execute the computer executable instructions to monitor login eventsat the login webpage.

FIG. 15 is an example apparatus that may be used to implement theimpression monitor system 102 of FIGS. 1-3. In the illustrated example,to detect whether AME cookies (e.g., the AME cookie 208 of FIG. 2) havebeen set (e.g., are stored) in client devices (e.g., any of the clientdevices 108 of FIGS. 1-3), the impression monitor system 102 is providedwith a cookie status detector 1402. For example, the cookie statusdetector 1402 may inspect or analyze messages (e.g., the request 206 ofFIG. 2) from client devices to determine whether AME cookies are presenttherein. In the illustrated example, to generate AME cookies (e.g., theAME cookie 208 (FIG. 2)), the impression monitor system 102 is providedwith a cookie generator 1404.

In the illustrated example, to generate login timestamps (e.g., thelogin timestamp 220 of FIG. 2), the impression monitor system 102 isprovided with a timestamp generator 1406. For example, the timestampgenerator 1406 may be implemented using a real-time clock (RTC) or anyother timing or clock device or interface to track time and generatetimestamps. In the illustrated example, to generate messages (e.g., theresponse 216 of FIG. 2), the impression monitor system 102 is providedwith a message generator 1408. In the illustrated example, to logimpressions, the impression monitor system 102 is provided with animpression logger 1410. For example, the impression logger 1410 may logimpressions in the AME impression store 114 as shown in FIG. 6.

In the illustrated example, to receive messages and/or information fromclient devices 108 and send messages and/or information to clientdevices 108 and/or to partner database proprietors 104 a and 104 b, theimpression monitor system 102 is provided with a communication interface1412. For example, the communication interface 1412 may receive messagessuch as the tag request(s) 112 (FIG. 1) and the request 206 (FIG. 2)from client devices 108. Additionally, the communication interface 1412may send messages such as the response 216 (FIG. 2) to the clientdevices 108 and send logged impressions (e.g., impressions logged in theAME impression store 114) to partner database proprietors 104 a and 104b.

FIG. 15 is an example apparatus that may be used to implement a cookiereporter 202 of FIG. 2. In the illustrated example, to detect logevents, the cookie reporter 202 is provided with a login event detector1502. For example, the login detector 1502 may be configured to monitorlogin events generated by web browsers (e.g., the web browser 302 ofFIG. 3) of client devices (e.g., the client devices 108 of FIGS. 1-3).In the illustrated example, when a user logs in to the login webpage 204of FIG. 2, the login detector 1502 detects a login event.

In the illustrated example, to detect whether AME cookies (e.g., the AMEcookie 208 of FIG. 2) or partner cookies (e.g., the partner cookie 228of FIG. 2) have been set (e.g., are stored) in client devices (e.g., theclient devices 108 of FIGS. 1-3), the cookie reporter 202 is providedwith a cookie status detector 1504. For example, the cookie statusdetector 1502 may inspect or analyze cookie files or cookie entries inclient devices to determine whether AME cookies (e.g., the AME cookie208 of FIG. 2) or partner cookies (e.g., the partner cookie 228 of FIG.2) have been previously set. In the illustrated example, the cookiestatus detector 1504 may also determine whether cookies have expired. Inthe illustrated example, when a cookie expires, it is treated as invalidor as if it no longer exists in a client device and must be set again bya corresponding server domain.

In the illustrated example, to retrieve cookies from storage locationsin client devices (e.g., the client devices 108 of FIGS. 1-3), thecookie reporter 202 is provided with a cookie interface 1506. Forexample, the cookie interface 1506 may retrieve AME cookies (e.g., theAME cookie 208 of FIG. 2) or partner cookies (e.g., the partner cookie228 of FIG. 2) from their respective storage locations in clientdevices. In addition, the cookie interface 1506 may also store cookiesset by and received from the impression monitor system 102 and/or anypartner database proprietor in the client devices.

In the illustrated example, to generate messages (e.g., the tagrequest(s) 112 of FIGS. 1 and 3, the log reporting messages 118 of FIGS.1 and 2, and the request 206 of FIG. 2), the cookie reporter 202 isprovided with a message generator 1508. In the illustrated example, tosend messages and/or information to the impression monitor system 102and/or to partner database proprietors (e.g., the partner databaseproprietors 104 a and 104 b of FIGS. 1 and 2) and/or to receive messagesand/or information from the impression monitor system 102, the cookiereporter 202 is provided with a communication interface 1510. Forexample, the communication interface 1510 may send the tag request(s)112 (FIGS. 1 and 3) and the request 206 of FIG. 2 to the impressionmonitor system 102, receive the response 216 (FIG. 2) from theimpression monitor system 102, and send the login reporting messages 118(FIGS. 1 and 2) to the partner database proprietors 104 a and 104 b.

While example manners of implementing the apparatus 102 and 202 havebeen illustrated in FIGS. 14 and 15, one or more of the elements,processes and/or devices illustrated in FIGS. 14 and 15 may be combined,divided, re-arranged, omitted, eliminated and/or implemented in anyother way. Further, the cookie status detector 1402, the cookiegenerator 1404, the timestamp generator 1406, the message generator1408, the impression logger 1410, the communication interface 1412and/or, more generally, the example apparatus 102 of FIG. 14 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. In addition, the login eventdetector 1502, the cookie status detector 1504, the cookie interface1506, the message generator 1508, the communication interface 1510and/or, more generally, the example apparatus 202 of FIG. 15 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of the cookiestatus detector 1402, the cookie generator 1404, the timestamp generator1406, the message generator 1408, the impression logger 1410, thecommunication interface 1412 and/or, more generally, the exampleapparatus 102 and/or any of the login event detector 1502, the cookiestatus detector 1504, the cookie interface 1506, the message generator1508, the communication interface 1510 and/or, more generally, theexample apparatus 202 could be implemented by one or more circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the apparatusor system claims of this patent are read to cover a purely softwareand/or firmware implementation, at least one of the cookie statusdetector 1402, the cookie generator 1404, the timestamp generator 1406,the message generator 1408, the impression logger 1410, thecommunication interface 1412, the login event detector 1502, the cookiestatus detector 1504, the cookie interface 1506, the message generator1508, and/or the communication interface 1510 are hereby expresslydefined to include a tangible computer readable medium such as a memory,DVD, CD, BluRay disk, etc. storing the software and/or firmware. Furtherstill, the example apparatus 102 and 202 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 14 and 15, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

Turning to FIG. 8, an example impressions totalization data structure800, which may be generated by the report generator 412 of FIG. 4,stores impression totalizations based on the impressions logged by theimpression monitor system 102 (FIGS. 1-3). As shown in FIG. 8, theimpressions totalization structure 800 shows quantities of impressionslogged for the client devices 108 (FIGS. 1-3). In the illustratedexample, the impressions totalization structure 800 is generated by thereport generator 412 for an advertisement campaign (e.g., one or more ofthe advertisements 110 of FIG. 1) to determine frequencies ofimpressions per day for each monitored user.

To track frequencies of impressions per unique user per day, theimpressions totalization structure 800 is provided with a frequencycolumn 802. A frequency of 1 indicates one exposure per day of an adcampaign to a unique user, while a frequency of 4 indicates fourexposures per day of the same ad campaign to a unique user. To track thequantity of unique users to which impressions are attributable, theimpressions totalization structure 800 is provided with a UUIDs column804. A value of 100,000 in the UUIDs column 804 is indicative of 100,000unique users. Thus, the first entry of the impressions totalizationstructure 800 indicates that 100,000 unique users (i.e., UUIDs=100,000)were exposed once (i.e., frequency=1) in a single day to a particular adcampaign.

To track impressions based on exposure frequency and UUIDs, theimpressions totalization structure 800 is provided with an impressionscolumn 806. Each impression count stored in the impressions column 806is determined by multiplying a corresponding frequency value stored inthe frequency column 802 with a corresponding UUID value stored in theUUID column 804. For example, in the second entry of the impressionstotalization structure 800, the frequency value of two is multiplied by200,000 unique users to determine that 400,000 impressions areattributable to a particular ad campaign.

Turning to FIG. 9, an ad campaign-level age/gender and impressioncomposition data structure 900 is shown, which, in the illustratedexample, may be generated by the report generator 412 of FIG. 4. Theimpression data in the ad campaign-level age/gender and impressioncomposition structure 900 of FIG. 9 corresponds to impressionsattributable to registered user of a particular partner database (DB)proprietor (e.g., the partner A database proprietor 104 a of FIGS. 1 and2 or the partner B database proprietor 104 b of FIG. 1). Similar tablescan be generated for content and/or other media. Additionally oralternatively, other media in addition to advertisements may be added tothe data structure 900.

The ad campaign-level age/gender and impression composition structure900 is provided with an age/gender column 902, an impressions column904, a frequency column 906, and an impression composition column 908.The age/gender column 902 of the illustrated example indicates differentage/gender demographic groups. The impressions column 904 of theillustrated example stores values indicative of the total impressionsfor a particular ad campaign for corresponding age/gender demographicgroups. The frequency column 906 of the illustrated example storesvalues indicative of the frequency of exposure per user for the adcampaign that contributed to the impressions in the impressions column904. The impressions composition column 908 of the illustrated examplestores the percentage of impressions for each of the age/genderdemographic groups.

In some examples, the demographics analyzer 406 and the demographicsmodifier 408 of FIG. 4 perform demographic accuracy analyses andadjustment processes on demographic information before tabulating finalresults of impression-based demographic information in thecampaign-level age/gender and impression composition table 900. This canbe done to address a problem facing online audience measurementprocesses in that the manner in which registered users representthemselves to online database proprietors (e.g., the partners 104 a and104 b) is not necessarily veridical (e.g., truthful and/or accurate). Insome instances, example approaches to online measurements that leverageaccount registrations at such online database proprietors to determinedemographic attributes of an audience may lead to inaccuratedemographic-exposure results if they rely on self-reporting ofpersonal/demographic information by the registered users during accountregistration at the database proprietor site. There may be numerousreasons for why users report erroneous or inaccurate demographicinformation when registering for database proprietor services. Theself-reporting registration processes used to collect the demographicinformation at the database proprietor sites (e.g., social media sites)does not facilitate determining the veracity of the self-reporteddemographic information. In some examples, to analyze and/or adjustinaccurate demographic information, the demographics analyzer 406 and/orthe demographics modifier 408 may use example methods, systems,apparatus, and/or articles of manufacture disclosed in U.S. patentapplication Ser. No. 13/209,292, filed on Aug. 12, 2011, and titled“Methods and Apparatus to Analyze and Adjust Demographic Information,”which is hereby incorporated herein by reference in its entirety.

Although the example ad campaign-level age/gender and impressioncomposition structure 900 shows impression statistics in connection withonly age/gender demographic information, the report generator 412 ofFIG. 4 may generate the same or other data structures to additionally oralternatively include other types of demographic information. In thismanner, the report generator 412 can generate the impression reports 106a (FIGS. 1 and 4) to reflect impressions based on different types ofdemographics and/or different types of media.

FIGS. 10-13 are flow diagrams representative of machine readableinstructions that can be executed to implement the apparatus and systemsof FIGS. 1, 2, 3, and/or 4. The example processes of FIGS. 10-13 may beimplemented using machine readable instructions that, when executed,cause a device (e.g., a programmable controller or other programmablemachine or integrated circuit) to perform the operations shown in FIGS.10-13. In this example, the machine readable instructions comprise aprogram for execution by a processor such as the processor 2912 shown inthe example computer 2910 discussed below in connection with FIG. 29.The program may be embodied in software stored on a tangible computerreadable medium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a BluRay disk, a flash memory, a read-only memory(ROM), a random-access memory (RAM), or a memory associated with theprocessor 2912, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 2912and/or embodied in firmware or dedicated hardware.

As used herein, the term tangible computer readable medium is expresslydefined to include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIGS. 10-13 may be implemented using coded instructions(e.g., computer readable instructions) stored on a non-transitorycomputer readable medium such as a flash memory, a read-only memory(ROM), a random-access memory (RAM), a cache, or any other storage mediain which information is stored for any duration (e.g., for extended timeperiods, permanently, brief instances, for temporarily buffering, and/orfor caching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

Alternatively, the example processes of FIGS. 10-13 may be implementedusing any combination(s) of application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)), field programmablelogic device(s) (FPLD(s)), discrete logic, hardware, firmware, etc.Also, the example processes of FIGS. 10-13 may be implemented as anycombination(s) of any of the foregoing techniques, for example, anycombination of firmware, software, discrete logic and/or hardware.

Although the example processes of FIGS. 10-13 are described withreference to the flow diagrams of FIGS. 10-13, other methods ofimplementing the apparatus and systems of FIGS. 1, 2, 3, and/or 4 may beemployed. For example, the order of execution of the blocks may bechanged, and/or some of the blocks described may be changed, eliminated,sub-divided, or combined. Additionally, one or both of the exampleprocesses of FIGS. 10-13 may be performed sequentially and/or inparallel by, for example, separate processing threads, processors,devices, discrete logic, circuits, etc.

Turning in detail to FIG. 10, the depicted example processes may be usedto report login events and user cookies (e.g., the AME cookie 208 andthe partner A cookie 228 of FIGS. 2 and 3) to database proprietors(e.g., the partner A database proprietor 104 a of FIGS. 1 and 2). In theillustrated example, the flow diagram shows a client device process 1002and an impression monitor system process 1004. In the illustratedexample, the client device process 1002 may be performed by the cookiereporter 202 of FIGS. 2 and 15, and the impression monitor systemprocess 1004 may be implemented by the impression monitor system 102 ofFIGS. 1-3 and 14. The example processes of FIG. 10 are described inconnection with FIG. 2 as interactions between the client device 108,the impression monitor system 102, and the partner A database proprietor104 a. However, processes similar or identical to the example processesof FIG. 10 may be performed at any time or at the same time betweenother client devices, the impression monitor system 102 and/or otherdatabase proprietors to accomplish the same type of user login reportingevents when users login to login pages (e.g., the login page 204 of FIG.2) of respective database proprietors (e.g., the database proprietors104 a and 104 b of FIGS. 1 and 2).

Initially, as part of the client device process 1002, the login eventdetector 1502 (FIG. 15) detects a login event (block 1006). The loginevent may be, for example, a user of the client device 108 logging intothe login page 204 of FIG. 2. The message generator 1508 (FIG. 15)generates the request 206 (FIG. 2) to indicate the login event (block1008). The cookie status detector 1504 (FIG. 15) determines whether anAME cookie (e.g. the AME cookie 208 of FIG. 2) is already set in theclient device 108 (block 1010). If the AME cookie 208 is already set,the cookie interface 1506 (FIG. 15) and/or the message generator 1508store(s) the AME cookie 208 (e.g., a name-value pair identifying a user)in the request 206 (block 1012). After storing the AME cookie 208 in therequest 206 (block 1012) or if the AME cookie 208 is not already set inthe client device (block 1010), the communication interface 1510 (FIG.15) sends the request 206 to the impression monitor system 102 (block1014).

As shown in the example impression monitor system process 1004, thecommunication interface 1412 (FIG. 14) receives the request 206 (block1016), and the cookie status detector 1402 (FIG. 14) determines whetherthe AME cookie 208 is already set in the client device 108 (block 1018).For example, the cookie status detector 1402 can determine whether theAME cookie 208 is already set based on whether the request 206 containsthe AME cookie 208. If the cookie status detector 1402 determines thatthe AME cookie 208 is not already set (block 1018), the cookie generator1404 (FIG. 14) creates the AME cookie 208 (block 1020). For example, thecookie generator 1404 can generate the AME cookie 208 by generating aUUID for the client device 108. The message generator 1408 (FIG. 14)stores the AME cookie 208 in the response 216 (FIG. 2) (block 1022).

After storing the AME cookie 208 in the response 216 (block 1022) or ifthe cookie status detector 1402 determines at block 1018 that the AMEcookie 208 is already set in the client device 108, the timestampgenerator 1406 generates a login timestamp (e.g., the login timestamp220 of FIG. 2) (block 1024) to indicate a login time for the detectedlogin event. The message generator 1408 stores the login timestamp 220in the response 216 (block 1026), and the communication interface 1412sends the response 216 to the client device 108 (block 1028).

Returning to the client device process 1002, the communication interface1510 (FIG. 15) receives the response 216 (block 1030), and the messagegenerator 1508 (FIG. 15) generates the login reporting message 118(FIGS. 1 and 2) (block 1032). If present, the cookie interface 1506(FIG. 15) and/or the message generator 1508 store(s) a partner cookiecorresponding to the login event detected at block 1006 (e.g., thepartner A cookie 228) in the login reporting message 118 (block 1034).If a corresponding partner cookie is not present in the client device108, a partner cookie is not stored in the login reporting message 118to indicate to the corresponding partner that it should create a partnercookie for the client device 108. In addition, the cookie interface 1506and/or the message generator 1508 store(s) the AME cookie 208 as a dataparameter (e.g., in the payload 232) in the login reporting message 118(block 1036). The message generator 1508 also stores the login timestamp220 in the login reporting message 118 (e.g., in the payload 232) (block1038). The communication interface 1510 sends the login reportingmessage 118 to a corresponding partner database proprietor (e.g., thepartner A database proprietor 104 a) (block 1040). In this manner, thecookie reporter 202 enables the partner A database proprietor 104 a tomap the partner A cookie 228 to the AME cookie 208 and the logintimestamp 220 in the partner cookie map 236 of FIGS. 2 and 5. Theexample process of FIG. 10 then ends.

Turning now to FIG. 11, the depicted flow diagram is representative ofan example process that may be performed by a partner databaseproprietor (e.g., the partner database proprietors 104 a and/or 104 b ofFIGS. 1 and 2) to map AME cookie identifiers (e.g., a UUID of the AMEcookie 208 of FIG. 2) with user identifiers (e.g., a UUID of the partnerA cookie 228 of FIG. 2) of users registered with the partner databaseproprietor. While for simplicity, FIG. 11 refers to a process receivinga single login message, many such processes may exist and execute inparallel (e.g., parallel threads). The example process of FIG. 11 isdescribed in connection with the illustrated example of FIG. 2, theapparatus 400 of FIG. 4, and the partner A database proprietor 104 a.However, processes similar or identical to the example processes of FIG.11 may be performed at any time or at the same time by other partnerdatabase proprietors and/or other apparatus to accomplish the same typeof cookie mapping process.

Initially, the partner A database proprietor 104 a receives the loginreporting message 118 (FIGS. 1 and 2) (block 1102). The data parser 414(FIG. 4) extracts the partner A cookie 228 (block 1104) from the loginreporting message 118. In the illustrated example, the data parser 414extracts the partner A cookie 228 from the cookie field 230 of the loginreporting message 118. The data parser 414 extracts the AME cookie 208(block 1106) from the login reporting message 118. In the illustratedexample, the data parser 414 extracts the AME cookie 208 as a dataparameter from the payload 232 of the login reporting message 118. Inaddition, the data parser 414 extracts the login timestamp 220 from thelogin reporting message 118 (block 1108). The mapper 416 (FIG. 4) mapsthe partner A cookie 228 to the AME cookie 208 (e.g., maps the UUIDs ofeach cookie to one another) (block 1110) in, for example, the partnercookie map 236 of FIGS. 2 and 5. In addition, the mapper 416 stores thelogin timestamp 220 in association with the mapped cookies (block 1112)in the partner cookie map 236. The example process of FIG. 11 then ends.

Now turning to FIG. 12, the depicted example process may be performed tolog impressions. In the illustrated example, the example process of FIG.12 is described in connection with FIGS. 3 and 14 as being performed bythe impression monitor system 102 based on tag requests received fromthe client device 108. However, processes similar or identical to theexample process of FIG. 12 may be performed at any time or at the sametime (e.g., multiple threads may be spawned and execute in parallel) bythe impression monitor system 102 in connection with other clientdevices (e.g., any of the client devices 108 of FIG. 1 or any otherclient devices) to log impressions attributable to those client devices.

Initially, the communication interface 1412 (FIG. 14) receives a tagrequest (e.g., the tag request 112 of FIGS. 1 and 3) (block 1202). Theimpression logger 1410 (FIG. 14) logs an impression for an AME UUIDindicated by the AME cookie 208 (block 1204). In the illustratedexample, the impression logger 1410 logs the impression in the AMEimpression store 114 of FIGS. 1, 3, and 6. The impression logger 1410determines whether it should send the AME impression logs 122 (FIGS. 1and 4) to one or more partner database proprietors (block 1206). Forexample, the impression logger 1410 may be configured to periodically oraperiodically send the AME impression logs 122 to one or more partnerdatabase proprietors (e.g., the partner database proprietors 104 a and104 b of FIGS. 1 and 2) based on one or more of a schedule and/or athreshold of logged impressions.

If the impression logger 1410 determines that it should send the AMEimpression logs 122 to one or more partner database proprietors (block1206), the communication interface 1412 sends the AME impression logs122 to the one or more partner database proprietors (block 1208). Inresponse, the communication interface 1412 receives one or moreimpression reports (e.g., the impression reports 106 a and 106 b ofFIGS. 1 and 4) from the one or more partner database proprietors (block1210). In some examples, the impression monitor system 102 appliesweighting factors to impression audience data in impression reports fromdifferent database proprietors (e.g., the partner database proprietors104 a and 104 b). In some examples, the weighting factors are determinedfor each database proprietor based on, for example, demographicdistributions and/or impression distributions in the impression dataand/or sample sizes (e.g., the quantity of registered users of aparticular database proprietor, the quantity of registered usersmonitored for the particular database proprietor, and/or the quantity ofimpressions logged by the AME 103 for registered users of the particulardatabase proprietor).

After receiving the one or more impression reports (block 1210) or if atblock 1206 the impression logger 1410 determines that it should not sendthe AME impression logs 122 to one or more partner database proprietors,the impression monitor system 102 determines whether it should continueto monitor impressions (block 1212). For example, the impression monitorsystem 102 may be configured to monitor impressions until it is turnedoff or disabled. If the impression monitor system 102 determines that itshould continue to monitor impressions (block 1212), control returns toblock 1202. Otherwise, the example process of FIG. 12 ends.

Turning now to FIG. 13, the depicted example process may be used togenerate demographics-based impressions reports (e.g., the impressionreports 106 a and 106 b of FIGS. 1 and 4). The example process of FIG.13 is described in connection with FIG. 4 as being implemented by theexample apparatus 400 via the partner A database proprietor 104 a.However, processes similar or identical to the example process of FIG.13 may be performed at any time or at the same time by any other partnerdatabase proprietor(s) to generate impression reports based onregistered users of those partner database proprietor(s).

Initially, the apparatus 400 receives the AME impression logs 122 (FIG.4) (block 1302). The cookie matcher 402 (FIG. 4) matches AME cookies topartner database proprietor cookies (block 1304). For example, thecookie matcher 402 can use a cookie map of the corresponding databaseproprietor (e.g., the partner A cookie map 236 (FIG. 4)) to match UUIDsfrom AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3) indicatedin the AME impression logs 122 to UUIDs from partner database proprietorcookies (e.g., the partner A database proprietor cookie 228 of FIGS. 2and 3). The cookie matcher 402 then associates impressions (e.g.,impressions noted in the AME impression logs 122) to correspondingpartner database proprietor UUIDs (block 1306) based on matches found atblock 1304. For example, the cookie matcher 402 may generate thepartner-based impressions data structure 700 described above inconnection with FIG. 7.

The demographics associator 404 (FIG. 4) associates demographics ofregistered users of the corresponding database proprietor (e.g., thepartner A database proprietor 104 a) to the impressions (block 1308).For example, the demographics associator 404 may retrieve demographicsinformation from the user accounts database 238 (FIGS. 2 and 4) forpartner user IDs noted in the partner user ID column 712 of thepartner-based impressions data structure 700.

The user ID modifier 410 removes user IDs from the demographics-basedimpressions data structure 700 (block 1310). For example, the user IDmodifier 410 can remove UUIDs from the AME user ID column 702corresponding to AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3)and the partner user ID column 712 corresponding to partner cookies(e.g., the partner A cookie 228 of FIGS. 2 and 3). In other examples,the report generator 412 can copy selected portions from thedemographics-based impressions data structure 700 and store the selectedportions in a report without copying over the user IDs. In this manner,the apparatus 400 can obfuscate identities of registered users toprotect their privacy when the demographics-based impressions are sharedwith others (e.g., an audience measurement entity).

The demographics analyzer 406 (FIG. 4) analyzes the demographicsinformation (block 1312) that was associated with the impressions atblock 1308. The demographics analyzer 406 determines whether anydemographics information needs to be modified (block 1314). If any ofthe demographics information needs to be modified (e.g., demographicsinformation needs to be changed or added due to being incomplete and/orinaccurate), the demographics modifier 408 (FIG. 4) modifies selectdemographics data needing modification (block 1316). In the illustratedexample, the demographics analyzer 406 and/or the demographics modifier408 may perform the operations of blocks 1312, 1314, and 1316 to analyzeand/or modify demographics information using, for example, one or moreexample techniques disclosed in U.S. patent application Ser. No.13/209,292, filed on Aug. 12, 2011, and titled “Methods and Apparatus toAnalyze and Adjust Demographic Information,” which is herebyincorporated herein by reference in its entirety.

After modifying demographics information at block 1316 or if at block1314 the demographics analyzer 406 determines that none of thedemographics information requires modification, the report generator 412generates one or more impression reports (e.g., the impression reports106 a of FIGS. 1 and 4) (block 1318). For example, the report generator412 may generate one or more of the impression reports 106 a using oneor more example techniques described above in connection with FIGS. 8and 9 and/or using any other suitable technique(s). The apparatus 400then sends the one or more impression reports 106 a to the impressionmonitor system 102 (block 1320). In the illustrated example, theimpression reports 106 a are indicative of demographic segments,populations, or groups associated with different AME cookies 208 (andcorresponding partner A cookies 228) and that were exposed to media(e.g., advertisements, content, and/or other media) identified bycampaign IDs and/or other the media content IDs. The example process ofFIG. 13 then ends.

FIG. 16 is a block diagram of an example system 1600 to generate anadjustment factor. For example, the system 1600 may generate anadjustment factor to determine more accurate information based on theinformation generated in accordance with the methods and apparatusdescribed in conjunction with FIGS. 1-15. The example system 1600includes one or more panelist meter(s) 1602, a datastore 1604, a cookieto panelist matcher 1606, a panelist to session matcher 1608, a partnersessions pageview analyzer 1610, a panelist sessions pageview analyzer1612, and an adjustment factor generator 1614.

The panelist meter(s) 1602 collect information about computing activityon traditional panelists' computers. According to the illustratedexample, the panelist meter(s) 1602 are implemented by software that isinstalled on traditional panelists' computers. Alternatively, any othertype of panelist meter(s) 1602 may be utilized. For example, thepanelist meter(s) 1602 may be partly or entirely implemented by a deviceassociated with a computer.

The panelist meter(s) 1602 of the illustrated example collectinformation about computing sessions. For example, a computing sessionmay begin when a user logs into the computer, when a user opens a webbrowser, when the user requests media from a media provider, when a useridentifies themselves to the panelist meter(s) 1602, etc. The panelistmeter(s) 1602 of the illustrated example determine a user associatedwith a computing session by prompting a user to identify themself. Thepanelist meter(s) 1602 also determine an end of a computing session. Forexample, the panelist meter(s) 1602 may determine that a computingsession has ended when a user logs out of the computer, when a usercloses a web browser, after a period of time in which there is no userinput to the computer, etc. The computing session information is storedin the datastore 1604. For the computing session information may bestored in a table as shown in FIG. 17.

According to the example illustrated in FIG. 17, the table includes ameter_id field that identifies the panelist meter that collected theinformation, a member id field identifying the user associated with thecomputing session, a computer_id field identifying the computer, astart_time field identifying the start of the computing session, and anend_time field identifying the end of the computing session.

The panelist meter(s) 1602 of the illustrated example also collectsinformation about requests to and responses from media providers. Thepanelist meter(s) 1602 also collect information about cookies thatidentify a user to a media provider and/or a partner database provider.For example, when a tag request is sent to a partner database provider,the tag request and a cookie identifying the user to the partnerdatabase provider (if one exists) are logged by the panelist meter(s)1602. In some examples, the cookie is only logged when it is set on thecomputer instead of logging the cookie each time it is sent with a tagrequest. The logged information is stored in the datastore 1604. Forexample, the logged information may be stored as shown in FIG. 18.

According to the example illustrated in FIG. 18, the logged informationincludes a cookie_value field identifying the recorded cookie value, ameter_id field that identifies the panelist meter that logged theinformation, a computer_id field identifying the computer, and alocal_time field indicating the time that the cookie value wasestablished (e.g., the time that the user logged in and the cookie wasset on the user's computer).

The datastore 1604 of the illustrated example of FIG. 16 stores datareceived from the panelist meter(s) 1602 and transmits the data to oneor more of the cookie to panelist matcher 1606, the partner to sessionmatcher 1608, and the panelist sessions pageview analyzer 1612. In someexamples, the datastore 1604 may also store data generated by one ormore of the cookie to panelist matcher 1606, the panelist to sessionmatcher 1608, the partner sessions pageview analyzer 1610, and thepanelist sessions pageview analyzer 1612. The datastore 1604 mayadditionally or alternatively store data from or transmit data to anyother element.

The cookie to panelist matcher 1606 of the illustrated example analyzesthe information about computing sessions and the information aboutpartner cookies from the panelist meter(s) 1602 to determine anassociation of partner cookies and panelist members. The example cookieto panelist matcher 1606 compares the time at which a partner cookie isset (e.g., the time identified in the table of FIG. 18) to computingsession start and end times (e.g., the start and end times in the tableof FIG. 17) to determine an association of cookies to computingsessions. The example cookie to panelist matcher 1606 also determines apanelist member identified for matching sessions (e.g., from the memberID in the table of FIG. 17).

The cookie to panelist matcher 1606 of the illustrated example subtotalsthe number of times that a cookie is associated with each panelistmember to generate the table of FIG. 19. As shown in the example tableof FIG. 19, cookie 100000964240495 is associated with computing sessionsof member ID 1 twice and is associated with member ID 2 once. Accordingto the illustrated example, the cookie to panelist matcher 1606determines that cookie 100000964240495 is associated with member ID 1because the count for member ID 1 is greater than the count for memberID 2. Accordingly, the example cookie to panelist matcher 1606 generatesthe table of FIG. 20, which associates the cookie with the panelistmember. The association illustrated in the example of FIG. 20 indicatesa determination as to which panelist member is associated with aparticular partner cookie. This process enables demographic informationknown for the panelist member to be associated with the partner cookie.

The panelist to session matcher 1608 of the illustrated example utilizesthe panelist to partner cookie association from the cookie to panelistmatcher 1606 and the information about partner cookie instances from thepanelist meter(s) 1602 to determine the start and end of partner cookiesessions. An example partner cookie to panelist association isillustrated in FIG. 21 (this table is similar to the table illustratedin FIG. 20, but includes an additional panelist member for furtherexplanation). An example listing of partner cookie instances isillustrated in FIG. 22 (this table is similar to the table illustratedin FIG. 18, but includes an additional partner cookie for furtherexplanation). The example panelist to session matcher 1608 uses thepartner cookie instance times to generate a listing of partner cookiesessions as illustrated in FIG. 23. A flowchart of an example method forgenerating the listing of partner cookie sessions is described inconjunction with the flowchart of FIG. 28.

The partner sessions pageview analyzer 1610 of the illustrated exampledetermines demographic information associated with media providerpageviews using the listing of partner cookie sessions from the panelistto session matcher 1608. The demographic information for the pageviewssimulates the demographic information that would be associated with suchmedia provider pageviews using the methods and apparatus described inconjunction with FIGS. 1-15. The demographic information is determinedby retrieving the demographic information from the panelist informationdetermined to be associated with the partner cookies because thedemographic information from the partner is typically not available dueto privacy restrictions. The example partner sessions pageview analyzer1610 aggregates information based on gender and age to determine anumber of pageviews as shown in column 2402 of FIG. 24.

The panelist sessions pageview analyzer 1612 of the illustrated exampledetermines demographic information associated with media providerpageviews using the panelist member information determined by thepanelist meter(s) 1602. For example, where the panelist meter(s) 1602prompt users of the computing device to input their identity, thedemographic information utilized by the panelist sessions pageviewanalyzer 1612 is the demographic information associated with thepanelist member identified in response to the prompting. The examplepanelist sessions pageview analyzer 1612 aggregates information based ongender and age to determine a number of pageviews as shown in column2404 of FIG. 24. The identity of the user of the computing devicedetermined based on the panelist meter(s) 1602 represents the controlinformation against which the partner cookie demographic determinationis compared.

The adjustment factor generator 1614 of the illustrated example comparesthe pageview information from the partner sessions pageview analyzer1610 with the pageview information from the panelist sessions pageviewanalyzer 1612 to determine an adjustment factor. The adjustment factoris a correction value to be applied to pageview counts determined usingthe partner cookie and partner databases. In other words, the adjustmentfactor represents the statistical difference between demographicinformation determined using the partner cookie (e.g., according to themethods and apparatus of FIGS. 1-15) and demographic informationdetermined from the panelist meter(s) 1602. For example, column 2406 ofFIG. 24 indicates an adjustment factor calculated for each demographiccategory by dividing the pageviews determined using demographicinformation from the partner cookie (column 2402) by the pageviewsdetermined using the panelist meter(s) 1602 demographic information(column 2404). The “ALL” row of FIG. 24 indicates that 10,810 pageviews(40,943-30,133) had no partner cookie associated with them, whichresults in an adjustment factor 74%. In other words, determiningpageviews using the partner cookie accounts for 74% of pageviews and anpageview count determined based on the partner cookies should be scaledaccording (e.g., pageviews determined using partner cookie should bedivided by 0.74) to account for pageviews by computing devices having nopartner cookie.

In some examples, the system 1600 may additionally or alternativelydetermine counts for unique users instead of individual pageviews bydetermining the number of unique users for a media provider using thepanelist meter(s) 1602 and the partner cookie information. An exampletable illustrating counts and adjustment factors for unique audience isillustrated in FIG. 25.

While the foregoing described of the system 1600 of FIG. 16 refers tomedia provider pageviews, any other computing activity may be analyzedand demographic information may be associated with the computingactivity.

FIGS. 26-29 are flow diagrams representative of machine readableinstructions that can be executed to implement the apparatus and systemsof FIG. 16. The example processes of FIGS. 26-29 may be implementedusing machine readable instructions that, when executed, cause a device(e.g., a programmable controller or other programmable machine orintegrated circuit) to perform the operations shown in FIGS. 26-29. Inthis example, the machine readable instructions comprise a program forexecution by a processor such as the processor 2912 shown in the examplecomputer 2910 discussed below in connection with FIG. 29. The programmay be embodied in software stored on a tangible computer readablemedium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a BluRay disk, a flash memory, a read-only memory(ROM), a random-access memory (RAM), or a memory associated with theprocessor 2912, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 2912and/or embodied in firmware or dedicated hardware.

As used herein, the term tangible computer readable medium is expresslydefined to include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIGS. 26-29 may be implemented using coded instructions(e.g., computer readable instructions) stored on a non-transitorycomputer readable medium such as a flash memory, a read-only memory(ROM), a random-access memory (RAM), a cache, or any other storage mediain which information is stored for any duration (e.g., for extended timeperiods, permanently, brief instances, for temporarily buffering, and/orfor caching of the information). As used herein, the term non-transitorycomputer readable medium is expressly defined to include any type ofcomputer readable medium and to exclude propagating signals. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. Thus, a claim using “at least” as thetransition term in its preamble may include elements in addition tothose expressly recited in the claim.

Alternatively, the example processes of FIGS. 26-29 may be implementedusing any combination(s) of application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)), field programmablelogic device(s) (FPLD(s)), discrete logic, hardware, firmware, etc.Also, the example processes of FIGS. 26-29 may be implemented as anycombination(s) of any of the foregoing techniques, for example, anycombination of firmware, software, discrete logic and/or hardware.

Although the example processes of FIGS. 26-29 are described withreference to the flow diagrams of FIGS. 26-29, other methods ofimplementing the apparatus and systems of FIG. 16 may be employed. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, sub-divided, orcombined. Additionally, one or both of the example processes of FIG. 16may be performed sequentially and/or in parallel by, for example,separate processing threads, processors, devices, discrete logic,circuits, etc.

The example process of FIG. 26 begins when the panelist meter(s) 1602 ofFIG. 16 determine cookie identifiers for partner logins (block 2602) anddetermine panel member identifiers for computing sessions (block 2604).For example, the panelist meter(s) 1602 may meter a computing device fora period of time collecting the identified information. The panelistmeter(s) 1602 may track the determined cookie identifiers for partnerlogins (block 2602) as illustrated in the example of FIG. 18. Thepanelist meter(s) may track the determined panel member identifiers forcomputing sessions (block 2604) as illustrated in the example of FIG.17. The information tracked by blocks 2602 and 2604 may be stored at thepanelist meter(s) 1602 and/or may be transmitted to the datastore 1604.

The example cookie to panelist 1606 then associates partner cookieidentifiers with panelist member identifiers (block 2606). An exampleprocess for associating partner cookie identifiers with panelist memberidentifiers is described in conjunction with FIG. 27. The association ofthe partner cookie identifiers may be stored in a table or other datastructure (e.g., the table illustrated in FIG. 20.

The example panelist to session matcher 1608 then associates paneliststo computing sessions using the association determined in block 2606(block 2608). While the panelist meter(s) 1602 associate panelists withcomputing sessions (e.g., by prompting users to identify themselves),the association of block 2608 determines (e.g., simulates) a matching ofpanelists (and their demographic information) that would be performed bythe methods and apparatus of FIGS. 1-15. An example process forassociating panelists to computing sessions is described in conjunctionwith FIG. 28.

Using the association from block 2608, the example partner sessionspageview analyzer 1610 determines pageviews by demographic group (block2610). The pageviews information of block 2610 is indicative of thepageview counts that would be determined using partner cookieinformation in accordance with the methods and apparatus of FIGS. 1-15.According to the illustrated example, the session information in thetable of FIG. 23 is used to associate pageviews at a particular timewith a panelist member associated with a computing session using thepartner cookie (rather than the panelist member identified by thepanelist meter(s) 1602). The pageviews are then aggregated bydemographic group.

Using panelist identity information from the panelist meter(s) 1602, theexample panelist sessions pageview analyzer 1612 determines pageviews bydemographic group (block 2612). The pageview information of block 2612represents the baseline pageview count by demographic information thatis assumed to be accurate. The recorded panelist member associated witha computer session (e.g., determined by prompting a user of a computingdevice) is utilized to determine demographic information associated withpageviews during a computing session. The pageviews are then aggregatedby demographic group.

The adjustment factor generator 1614 then compares pageviews based onpartner cookie information (from block 2608) to pageviews based onpanelist member (from block 2610) to determine adjustment factor(s) bydemographic group (block 2614). An example process for determiningadjustment factors is described in conjunction with FIG. 29.

In one example, the count of pageviews by demographic using panelistsession information (determined in block 2612) may be represented byP_(i,j), where i is the index for media providers and j is the index fordemographic groups. The count of pageviews by demographic using partnersession information (determined in block 2610) may be represented byP_(i,j) ^(PART). In such an example, the adjustment factor r_(i,j) ^(p)for media provider i and demographic group j is determined as

$r_{i,j}^{p} = {\frac{P_{i,j}^{PART}}{P_{i,j}}.}$

Similarly, an average monthly count of unique panelists belonging todemographic group may be represented by UA_(i,j), where i is the indexfor media providers and j is the index for demographic groups. Theaverage monthly count of unique panelists by demographic using partnersession information may be represented by UA_(i,j) ^(PART). In such anexample, the adjustment factor r_(i,j) ^(UA) for media provider i anddemographic group j is determined as

$r_{i,j}^{UA} = {\frac{{UA}_{i,j}^{PART}}{{UA}_{i,j}}.}$

In some examples, the adjustment factor is calculated at the category ofsub-category level (e.g., an adjustment factor may be calculated for allmedia providers in the News category). For example, the adjustmentfactor may be calculated at the sub-category when unique audience for agiven media provider and demographic group is less than 100.

FIG. 27 is a flowchart of an example process to associate partnercookies and panel member identifiers. According to the illustratedexample, the process of FIG. 27 is performed by the cookie to panelistmatcher 1606 of FIG. 16. The example process begins by selecting a firstpartner cookie identified on a list of cookie instances (block 2702).For example, the partner cookies may be identified on a list asillustrated in FIGS. 18 and/or 22. Next, the cookie instances arematched with computing sessions (e.g., computing sessions identified ina table as illustrated in FIG. 17) and the panelist member identifiersassociated with the matching computing sessions are subtotaled (block2704). For example, the member identifiers may be subtotaled asillustrated in FIG. 19. The panel member identifier with the greatestcount in the subtotal is selected (block 2706). An association of theselected partner cookie and the selected member identifier is recorded(block 2708). For example, the association may be recorded as shown inFIGS. 20 and 21.

After recording the association for the selected partner cookie, thecookie to panelist matcher 1606 determines if there are additionalpartner cookies to be processed (block 2710). If there are additionalpartner cookies to be processed, the next partner cookie is selected(block 2712) and control returns to block 2704 to process the partnercookie. If there are not additional partner cookies to be processed, theprocess of FIG. 27 completes. The completion of the process of FIG. 27may result in initiation of the process of FIG. 28.

FIG. 28 is a flowchart of an example process to determine sessioninformation for panelist members based on cookie to panelist memberassociation information determined by the process illustrated in FIG.27. According to the illustrated example, the process of FIG. 28 isperformed by the panelist to session matcher 1608 of FIG. 16. Theexample process begins by selecting a first partner cookie instance(block 2802). For example, the first partner cookie instance may beselected from a table of partner cookie instances such as the tableillustrated in FIGS. 18 and 22. Next, a panel member identifier for thecookie instance is determined using the cookie to panelist memberassociation information determined by the process illustrated in FIG. 27(block 2804). The time of the cookie instance is recorded as a sessionstart time for a computing session and the determined panel memberidentifier is associated with the session (block 2806). For example, thesession information may be recorded as illustrated in FIG. 23.

After recording the session information, the panelist to session matcher1608 determines if there are additional partner instances to beprocessed (block 2808). If there are additional partner cookie instancesto be processed, the next partner cookie instance is selected (block2810). The time of the newly selected cookie instance is recorded as thestop of the session for the previously selected panel member session(block 2812). In other words, the occurrence of each new cookie instanceindicates the termination of the previous cookie instance (and therebythe end of a generated panel member browsing session). Control thenreturns to block 2804 to process the newly selected cookie instance.

If there are not additional partner cookie instances to be processed(block 2808), the process of FIG. 28 completes. The completion of theprocess of FIG. 28 may result in initiation of one or more of blocks2610 and 2612.

In some examples, media (e.g., advertisements) is displayed across onseveral media providers in an advertising network. A measurement entitymay not know in advance which media providers will be displayingadvertisements. Furthermore, the demographics of different mediaproviders vary depending on the targeted demographic of the webpage(e.g., a sport news webpage vs. an entertainment news webpage).Accordingly, the panelist meter(s) 1602 capture the domain name wheremedia impressions appear (e.g., when the panelist meter(s) 1602 log animpression of an advertisement they also log the domain name of themedia provider on which the advertisement was displayed). Where media isdisplayed on both advertising networks and non-advertising networks(advertisements are provided directly to some media providers), thedomain name may be captured for a random sample (e.g., 20% ofimpressions).

To determine impressions for an advertising network a compositeadjustment factor that is a combination of media provider adjustmentfactors weighted by impression volume during presentation of the media(e.g., during an advertising campaign) is determined. In some examples,the composite adjustment factor is computed on a daily basis.

As previously described, the adjustment factor r_(i,j) ^(p) and theunique audience adjustment factor r_(i,j) ^(UA) are computed. Inaddition, a proportion of impressions of the advertising network thatare associated with a media provider i is represented by p_(i,j) ^(AN).For example, a particular media provider may account for 40% of countedimpressions (i.e., p_(i,j) ^(AN)=0.40). The impressions adjustmentfactor for advertising network AN and demographic group j is computed as

$r_{{AN},j}^{p} = {\sum\limits_{i}\;{p_{i}^{AN} \times r_{i,j}^{p}}}$and the unique audience adjustment factor is computed as

$r_{{AN},j}^{UA} = {\sum\limits_{i}\;{p_{i}^{AN} \times {r_{i,j}^{UA}.}}}$Thus, if there are two media providers in an advertising network andimpressions are distributed such that media provider A represents 40% ofimpressions and media provider B represents 60% of impressions, thecomposite adjustment factor for the advertising network is computed as0.4 multiplied by the adjustment factor for media provider A plus 0.6multiplied by the adjustment factor for media provider B. Such compositeadjustment factor can be computed for each of demographic group.

After computing adjustment factors, the adjustment factors can beapplied to collected monitoring data (e.g., the entire universe ofcollected, a subset of collected data, etc.). In the following example areporting entity is a media provider or an advertising network. Thefollowing measurement data may be determined by tagging and partner dataprovider measurement as described in conjunction with FIGS. 1-15.Impressions collected by webpage tagging for the United States forentity i may be represented by I_(i) and impression collected by webpagetagging for global sites may be represented by I_(i) ^(g). Unique cookiecounts for the United States may be represented by I_(UC) and uniquecookie counts for global sites may be represented by I_(UC) ^(g).Impressions determined using a partner database provider may berepresented by I_(i,j) ^(PART) and unique audience counts determinedusing a partner database provider may be represented by UA_(i,j) ^(FB).As described above, the impressions adjustment factor for entity i anddemographic group j may be represented by r_(i,j) ^(p) and the uniqueaudience impressions factor may be represented by r_(i,j) ^(UA).

An international exclusion factor is determined as

$R_{i,c}^{p} = {\frac{I_{i}}{I_{i}^{g}}.}$This value indicates the proportion of global entities represented bythe United States data.

To adjust the partner database provider data to match the total numberof impressions determined using tagging, a scaling factor is computed as

$S_{i}^{p} = {\frac{I_{i}}{\sum\limits_{j}\;\frac{I_{i,j}^{PART}}{r_{i,j}^{p}}}.}$Accordingly, the estimated impressions using the partner databaseprovider is determined as

${\overset{\sim}{I}}_{i,j} = {S_{i}^{p} \times {\frac{I_{i,j}^{PART}}{r_{i,j}^{p}}.}}$

The unique audience international exclusion factor is determined as

$R_{i,c}^{UA} = {\frac{I_{UC}}{I_{UC}^{g}}.}$This value indicates the proportion of unique audience coming from theUnited States. To determine US unique audience counts using data fromthe partner database provider, the unique audience internationalexclusion factor is applied across the demographic groups to data fromthe partner database provider.

In examples where a total unique audience measurement is not available,it may be assumed that the frequency observed for a partner databaseprovider is the same as the frequency for audience not observed by thepartner database provider. Accordingly, a raw observed frequency isdetermined as

$f = {\frac{R_{i,c}^{p} \times {\sum\limits_{j}\; I_{i,j}^{PART}}}{R_{i,c}^{UA} \times {\sum\limits_{j}\;{UA}_{i,j}^{PART}}}.}$The target total unique audience is determined as

$\frac{I_{i}}{f}.$Without scaling, the sum of the adjusted unique audience acrossdemographic groups is scaled by a scaling factor

$S_{i}^{UA} = {\frac{\left( \frac{I_{i}}{f} \right)}{\sum\limits_{j}\;\frac{{UA}_{i,j}^{PART}}{r_{i,j}^{UA}}}.}$Accordingly, the unique audience estimation is determined as

${\overset{\sim}{UA}}_{i,j} = {S_{i}^{UA} \times {\frac{{UA}_{i,j}^{PART}}{r_{i,j}^{UA}}.}}$

Once the data has been adjusted, the data can be grouped by campaign todetermine impressions and unique audience for a campaign. The set ofentities (e.g., media providers and/or advertising networks) belongingto a campaign is represented by S_(n), where n is an index of thecampaign. The estimated impressions for the campaign can be determinedas

${\overset{\sim}{I}}_{S_{n},j} = {\sum\limits_{i \in S_{n}}\;{{\overset{\sim}{I}}_{i,j}.}}$When determining the unique audience for a campaign, duplication acrosssites may be recognized. Accordingly, for each demographic group j andcampaign n, the campaign duplication factor is determined as

$d_{n,j} = \frac{{UA}_{S_{n,j}}^{PART}}{\sum\limits_{i \in S_{n}}\;{UA}_{i,j}^{PART}}$where d_(n,j) is less than 1. Accordingly, the unique audienceestimation for the campaign is determined as:

${\overset{\sim}{UA}}_{S_{n},j} = {d_{n,j} \times {\sum\limits_{i \in S_{n}}\;{{\overset{\sim}{UA}}_{i,j}.}}}$

While the foregoing examples describe particular equations fordetermining impressions and unique audience using calculated adjustmentfactors, any suitable equations may be used.

FIG. 29 is a block diagram of an example processor system 2910 that maybe used to implement the example apparatus, methods, and systemsdisclosed herein. As shown in FIG. 29, the processor system 2910includes a processor 2912 that is coupled to an interconnection bus2914. The processor 2912 may be any suitable processor, processing unit,or microprocessor. Although not shown in FIG. 29, the system 2910 may bea multi-processor system and, thus, may include one or more additionalprocessors that are identical or similar to the processor 2912 and thatare communicatively coupled to the interconnection bus 2914.

The processor 2912 of FIG. 29 is coupled to a chipset 2918, whichincludes a memory controller 2920 and an input/output (I/O) controller2922. A chipset provides I/O and memory management functions as well asa plurality of general purpose and/or special purpose registers, timers,etc. that are accessible or used by one or more processors coupled tothe chipset 2918. The memory controller 2920 performs functions thatenable the processor 2912 (or processors if there are multipleprocessors) to access a system memory 2924, a mass storage memory 2925,and/or an optical media 2927.

In general, the system memory 2924 may include any desired type ofvolatile and/or non-volatile memory such as, for example, static randomaccess memory (SRAM), dynamic random access memory (DRAM), flash memory,read-only memory (ROM), etc. The mass storage memory 2925 may includeany desired type of mass storage device including hard disk drives,optical drives, tape storage devices, etc. The optical media 2927 mayinclude any desired type of optical media such as a digital versatiledisc (DVD), a compact disc (CD), or a blu-ray optical disc. Theinstructions of any of FIGS. 10-13 may be stored on any of the tangiblemedia represented by the system memory 2924, the mass storage device2925, the optical media 2927, and/or any other media.

The I/O controller 2922 performs functions that enable the processor2912 to communicate with peripheral input/output (I/O) devices 2926 and2928 and a network interface 2930 via an I/O bus 2932. The I/O devices2926 and 2928 may be any desired type of I/O device such as, forexample, a keyboard, a video display or monitor, a mouse, etc. Thenetwork interface 2930 may be, for example, an Ethernet device, anasynchronous transfer mode (ATM) device, an 802.11 device, a digitalsubscriber line (DSL) modem, a cable modem, a cellular modem, etc. thatenables the processor system 2910 to communicate with another processorsystem.

While the memory controller 2920 and the I/O controller 2922 aredepicted in FIG. 29 as separate functional blocks within the chipset2918, the functions performed by these blocks may be integrated within asingle semiconductor circuit or may be implemented using two or moreseparate integrated circuits.

Although the above discloses example methods, apparatus, systems, andarticles of manufacture including, among other components, firmwareand/or software executed on hardware, it should be noted that suchmethods, apparatus, systems, and articles of manufacture are merelyillustrative and should not be considered as limiting. Accordingly,while the above describes example methods, apparatus, systems, andarticles of manufacture, the examples provided are not the only ways toimplement such methods, apparatus, systems, and articles of manufacture.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method comprising: determining an exposure of media for use by an audience measurement entity, wherein the exposure occurs after a cookie identifier is received at a computing device, wherein the cookie identifier is received from a database proprietor; determining, via a processor, a first panel member identifier associated with the exposure based on the cookie identifier; determining, via the processor, a second panel member identifier associated with the exposure based on a determination of a user identity by a panelist meter associated with the computing device; and determining, via the processor, an adjustment factor for use by the audience measurement entity by comparing the first panel member identifier and the second panel member identifier.
 2. A method as defined in claim 1, wherein the second panel member identifier is different than the first panel member identifier.
 3. A method as defined in claim 1, wherein determining the adjustment factor by comparing the first panel member identifier and the second panel member identifier comprises: incrementing a first count of exposures for a first demographic group associated with the first panel member identifier; incrementing a second count of exposures for a second demographic group associated with the second panel member identifier, wherein the first demographic group and the second demographic group are the same; and dividing the first count by the second count to determine the adjustment factor.
 4. A method as defined in claim 3, wherein the second count is incremented based on exposures associated with a plurality of panelist meters including the panelist meter.
 5. A method as defined in claim 1, wherein the exposure occurs before detecting a second cookie identifier established by the database proprietor.
 6. A method as defined in claim 1, wherein determining the first panel member identifier comprises: determining a plurality of computing sessions; determining a first estimated panel member identifier associated with a first subset of the computing sessions; determining a third panel member identifier associated with a second subset of the computing sessions; and determining that the cookie identifier is associated with the first panel member identifier because the cookie identifier is established during the first subset of the computing sessions more frequently than during the cookie identifier is established during the second subset of the computing sessions.
 7. A method as defined in claim 6, wherein determining the first panel member identifier further comprises: determining a start of an estimated computing session based on a time at which the cookie identifier is established; and determining an end of an estimated computing session based on a time at which a second cookie identifier is established.
 8. A method as defined in claim 1, wherein determining the exposure of the media comprises detecting that the media was received at the computing device.
 9. A method as defined in claim 1, further comprising applying the adjustment factor to monitoring data received from the database proprietor.
 10. A method as defined in claim 1, further comprising determining adjustment factors for each of a plurality of demographic groups.
 11. A method as defined in claim 1, further comprising: receiving a number of exposures associated with a demographic group from the database proprietor; and multiplying the adjustment factor by the number of exposures to determine an adjusted number of exposures.
 12. A method as defined in claim 1, further comprising: determining a first content provider and a second content provider associated with a campaign, wherein the adjustment factor is a first adjustment factor associated with the first content provider; determining a second adjustment factor; multiplying the adjustment factor by a first number of exposures determined for the first content provider to determine a first adjusted number of exposures; multiplying the second adjustment factor by a second number of exposures determined for the second content provider to determine a second adjusted number of exposures; and adding the first adjusted number of exposures and the second adjusted number of exposures to determine an adjusted number of exposures for the campaign.
 13. A method as defined in claim 12, further comprising determining a proportion of campaign exposures associated with the first content provider, wherein the adjusted number of exposures for the campaign is determined based on the proportion of campaign exposures associated with the first content provider.
 14. A method as defined in claim 1, wherein the adjustment factor is indicative of a difference between a number of exposures calculated using a plurality of panelist meters and a number of exposures calculated by the database proprietor.
 15. An apparatus comprising: a cookie to panelist matcher to determine a first panel member identifier associated with a cookie identifier received at a computing device, wherein the cookie identifier is received from a database proprietor; a partner sessions pageview analyzer to determine that a media exposure is associated with the first panel member identifier based on the cookie identifier; a panelist sessions pageview analyzer to determine that the media exposure is associated with a second panel member identifier based on a determination of a user identity; and an adjustment factor generator to compare the first panel member identifier and the second panel member identifier to determine an adjustment factor for use by an audience measurement entity.
 16. An apparatus as defined in claim 15, further comprising a panelist meter associated with the computing device to determine the user identity.
 17. An apparatus as defined in claim 15, wherein the second panel member identifier is different than the first panel member identifier.
 18. An apparatus as defined in claim 15, further comprising: the partner sessions pageview analyzer to increment a first count of exposures for a first demographic group associated with the first panelist identifier; the panelist sessions pageview analyzer to increment a second count of exposures for a second demographic group associated with the second panelist identifier, wherein the first demographic group and the second demographic group are the same; and wherein the adjustment factor generator is to compare the first panel member identifier and the second panel member identifier by dividing the first count by the second count to determine the adjustment factor.
 19. An apparatus as defined in claim 18, wherein the panelist sessions pageview analyzer is to increment the second count based on exposures associated with a plurality of panelist meters including the panelist meter.
 20. An apparatus as defined in claim 15, wherein the exposure occurs before the cookie to panelist matcher detects a second cookie identifier established by the database proprietor.
 21. An apparatus as defined in claim 15, wherein the partner sessions pageview analyzer is to determine the first panel member identifier by: determining a plurality of computing sessions; determining a first estimated panel member identifier associated with a first subset of the computing sessions; determining a third panel member identifier associated with a second subset of the computing sessions; and determining that the cookie identifier is associated with the first panel member identifier because the cookie identifier is established during the first subset of the computing sessions more frequently than during the cookie identifier is established during the second subset of the computing sessions.
 22. An apparatus as defined in claim 21, wherein the partner sessions pageview analyzer is further to determine the first panel member identifier by: determining a start of an estimated computing session based on a time at which the cookie identifier is established; and determining an end of an estimated computing session based on a time at which a second cookie identifier is established.
 23. An apparatus as defined in claim 15, wherein the cookie to panelist matcher is to determine the exposure of the media by detecting that the media was received at the computing device.
 24. An apparatus as defined in claim 15, wherein the adjustment factor generator is to apply the adjustment factor to monitoring data received from the database proprietor.
 25. An apparatus as defined in claim 15, wherein the adjustment factor generator is to determine adjustment factors for each of a plurality of demographic groups.
 26. An apparatus as defined in claim 15, wherein the adjustment factor generator is to: receive a number of exposures associated with a demographic group from the database proprietor; and multiply the adjustment factor by the number of exposures to determine an adjusted number of exposures.
 27. An apparatus as defined in claim 15, wherein the adjustment factor generator is to: determine a first content provider and a second content provider associated with a campaign, wherein the adjustment factor is a first adjustment factor associated with the first content provider; determine a second adjustment factor; multiply the first adjustment factor by a first number of exposures determined for the first content provider to determine a first adjusted number of exposures; multiply the second adjustment factor by a second number of exposures determined for the second content provider to determine a second adjusted number of exposures; and add the first adjusted number of exposures and the second adjusted number of exposures to determine an adjusted number of exposures for the campaign.
 28. An apparatus as defined in claim 27, wherein the adjustment factor generator is to determine a proportion of campaign exposures associated with the first content provider, and to determine the adjusted number of exposures for the campaign based on the proportion of campaign exposures associated with the first content provider.
 29. An apparatus as defined in claim 15, wherein the adjustment factor is indicative of a difference between a number of exposures calculated using a plurality of panelist meters and a number of exposures calculated by the database proprietor.
 30. A tangible computer readable medium comprising instructions that, when executed, cause a machine to at least: determine an exposure of media for use by an audience measurement entity, wherein the exposure occurs after a cookie identifier is received at a computing device, wherein the cookie identifier is received from a database proprietor; determine a first panel member identifier associated with the exposure based on the cookie identifier; determine a second panel member identifier associated with the exposure based on a determination of a user identity by a panelist meter associated with the computing device; and determine an adjustment factor for use by the audience measurement entity by comparing the first panel member identifier and the second panel member identifier.
 31. A tangible computer readable medium as defined in claim 30, wherein the second panel member identifier is different than the first panel member identifier.
 32. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to determine the adjustment factor by comparing the first panel member identifier and the second panel member identifier by: incrementing a first count of exposures for a first demographic group associated with the first panel member identifier; incrementing a second count of exposures for a second demographic group associated with the second panel member identifier, wherein the first demographic group and the second demographic group are the same; and dividing the first count by the second count to determine the adjustment factor.
 33. A tangible computer readable medium as defined in claim 32, wherein the instructions cause the machine to increment the second count based on exposures associated with a plurality of panelist meters including the panelist meter.
 34. A tangible computer readable medium as defined in claim 30, wherein the exposure occurs before detecting a second cookie identifier established by the database proprietor.
 35. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to determine the first panel member identifier by: determining a plurality of computing sessions; determining a first estimated panel member identifier associated with a first subset of the computing sessions; determining a third panel member identifier associated with a second subset of the computing sessions; and determining that the cookie identifier is associated with the first panel member identifier because the cookie identifier is established during the first subset of the computing sessions more frequently than during the second subset of the computing sessions.
 36. A tangible computer readable medium as defined in claim 35, wherein the instructions, when executed, cause the machine to determine the first panel member identifier by: determining a start of an estimated computing session based on a time at which the cookie identifier is established; and determining an end of an estimated computing session based on a time at which a second cookie identifier is established.
 37. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to determine the exposure of the media by detecting that the media was received at the computing device.
 38. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to apply the adjustment factor to monitoring data received from the database proprietor.
 39. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to determine respective adjustment factors for corresponding ones of a plurality of demographic groups.
 40. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to: receive a number of exposures associated with a demographic group from the database proprietor; and multiply the adjustment factor by the number of exposures to determine an adjusted number of exposures.
 41. A tangible computer readable medium as defined in claim 30, wherein the instructions, when executed, cause the machine to: determine a first content provider and a second content provider associated with a campaign, wherein the adjustment factor is a first adjustment factor associated with the first content provider; determine a second adjustment factor; multiply the first adjustment factor by a first number of exposures determined for the first content provider to determine a first adjusted number of exposures; multiply the second adjustment factor by a second number of exposures determined for the second content provider to determine a second adjusted number of exposures; and add the first adjusted number of exposures and the second adjusted number of exposures to determine an adjusted number of exposures for the campaign.
 42. A tangible computer readable medium as defined in claim 41, wherein the instructions, when executed, cause the machine to determine a proportion of campaign exposures associated with the first content provider, and to determine the adjusted number of exposures for the campaign based on the proportion campaign exposures associated with the first content provider.
 43. A tangible computer readable medium as defined in claim 30, wherein the adjustment factor is indicative of a difference between a number of exposures calculated using a plurality panelist meters and a number of exposures calculated by the database proprietor.
 44. A method as defined in claim 3, wherein the incrementing of the first count of exposures for the first demographic group associated with the first panel member identifier is performed by the database proprietor and the incrementing of the second count of exposures for the second demographic group associated with the second panel member identifier is performed by the audience measurement entity.
 45. An apparatus as defined in claim 18, wherein the partner sessions pageview analyzer is associated with the database proprietor and the panelist sessions pageview analyzer is associated with the audience measurement entity.
 46. A tangible computer readable medium as defined in claim 32, wherein the incrementing of the first count of exposures for the first demographic group associated with the first panel member identifier is performed by the database proprietor and the incrementing of the second count of exposures for the second demographic group associated with the second panel member identifier is performed by the audience measurement entity. 